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Since 1987, I have regularly divided my time between my job at McGill and GREQAM, a research lab in Marseille, France, associated with the Aix-Marseille School of Economics. My main research interests lie in the field of econometrics. I have worked for several years now on problems related to the bootstrap, in an attempt to improve the reliability and ease of implementation of this important statistical tool. 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id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Russell Davidson</h3></div><div class="js-work-strip profile--work_container" data-work-id="125630183"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630183/Essays_in_comparative_dynamics"><img alt="Research paper thumbnail of Essays in comparative dynamics" class="work-thumbnail" src="https://attachments.academia-assets.com/119637608/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630183/Essays_in_comparative_dynamics">Essays in comparative dynamics</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the second section of the essay, it is assumed that some externality arises which adversely af...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the second section of the essay, it is assumed that some externality arises which adversely affects urban life and which provokes people to move out to suburbs. The consequences of this are studied and two different kinds of dynamical evolution can be distinguished. One, in which house construction in the suburbs is slow enough to make it necessary for new construction to continue in the city, tends not to be disastrous for the city; the other, in which all urban construction stops when the externality arises, usually leads to complete decay of the city. Throughout the thesis there is an emphasis on the differences in approach between static or quasistatic problems and dynamic ones.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="804215c2bceb4c440fe36d065c940e69" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637608,"asset_id":125630183,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637608/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630183"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630183"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630183; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630182"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630182/Asymptotic_and_bootstrap_inference_for_inequality_and_poverty_measures"><img alt="Research paper thumbnail of Asymptotic and bootstrap inference for inequality and poverty measures" class="work-thumbnail" src="https://attachments.academia-assets.com/119637607/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630182/Asymptotic_and_bootstrap_inference_for_inequality_and_poverty_measures">Asymptotic and bootstrap inference for inequality and poverty measures</a></div><div class="wp-workCard_item"><span>Journal of Econometrics</span><span>, Nov 1, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A random sample drawn from a population would appear to offer an ideal opportunity to use the boo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples, although inference with poverty indices is satisfactory. We find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e939241d493b5771a22877e710f328f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637607,"asset_id":125630182,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637607/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630182"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630182; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630180"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630180/A_Parametric_Bootstrap_for_Heavy_Tailed_Distributions"><img alt="Research paper thumbnail of A Parametric Bootstrap for Heavy-Tailed Distributions" class="work-thumbnail" src="https://attachments.academia-assets.com/119637605/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630180/A_Parametric_Bootstrap_for_Heavy_Tailed_Distributions">A Parametric Bootstrap for Heavy-Tailed Distributions</a></div><div class="wp-workCard_item"><span>Econometric Theory</span><span>, Sep 8, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is known that Efron's resampling bootstrap of the mean of random variables with common distrib...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is known that Efron's resampling bootstrap of the mean of random variables with common distribution in the domain of attraction of the stable laws with infinite variance is not consistent, in the sense that the limiting distribution of the bootstrap mean is not the same as the limiting distribution of the mean from the real sample. Moreover, the limiting distribution of the bootstrap mean is random and unknown. The conventional remedy for this problem, at least asymptotically, is either the m out of n bootstrap or subsampling. However, we show that both these procedures can be quite unreliable in other than very large samples. A parametric bootstrap is derived by considering the distribution of the bootstrap P value instead of that of the bootstrap statistic. The quality of inference based on the parametric bootstrap is examined in a simulation study, and is found to be satisfactory with heavy-tailed distributions unless the tail index is close to 1 and the distribution is heavily skewed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="634578f32bbdaa6319946f83eb99550f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637605,"asset_id":125630180,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637605/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630180"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630180"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630180; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630168"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630168/Testing_for_Restricted_Stochastic_Dominance"><img alt="Research paper thumbnail of Testing for Restricted Stochastic Dominance" class="work-thumbnail" src="https://attachments.academia-assets.com/119637597/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630168/Testing_for_Restricted_Stochastic_Dominance">Testing for Restricted Stochastic Dominance</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance relation that we can seek to infer in many settings. One testing procedure that we consider is based on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the distributions under study that satisfy the null hypothesis, on the frontier between dominance and nondominance. These estimates can be used to perform dominance tests that can turn out to provide much improved reliability of inference compared with the asymptotic tests so far proposed in the literature.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44d550cfa820e23729eacc04d7e7807b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637597,"asset_id":125630168,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637597/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630168"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630168"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630168; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123020299"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123020299/Testing_for_Restricted_Stochastic_Dominance"><img alt="Research paper thumbnail of Testing for Restricted Stochastic Dominance" class="work-thumbnail" src="https://attachments.academia-assets.com/117554062/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123020299/Testing_for_Restricted_Stochastic_Dominance">Testing for Restricted Stochastic Dominance</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance relation that we can seek to infer in many settings. One testing procedure that we consider is based on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the distributions under study that satisfy the null hypothesis, on the frontier between dominance and nondominance. 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</script> <div class="js-work-strip profile--work_container" data-work-id="115150495"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150495/Wild_Bootstrap_Tests_for_IV_Regression"><img alt="Research paper thumbnail of Wild Bootstrap Tests for IV Regression" class="work-thumbnail" src="https://attachments.academia-assets.com/111642967/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150495/Wild_Bootstrap_Tests_for_IV_Regression">Wild Bootstrap Tests for IV Regression</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Aug 1, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a wild bootstrap procedure for linear regression models estimated by instrumental vari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1c35fc43ad6a906d88cd4c0b616af2f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642967,"asset_id":115150495,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642967/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150495"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150495; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115150493"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150493/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality"><img alt="Research paper thumbnail of Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality" class="work-thumbnail" src="https://attachments.academia-assets.com/111642966/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150493/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality">Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Apr 1, 1998</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We derive the asymptotic sampling distribution of various estimators frequently used to order dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7802f5f1716695f0e9c7c5e6a650e9eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642966,"asset_id":115150493,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642966/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150493"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150493"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150493; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115150491"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150491/Bootstrap_Tests_How_Many_Bootstraps"><img alt="Research paper thumbnail of Bootstrap Tests: How Many Bootstraps?" class="work-thumbnail" src="https://attachments.academia-assets.com/111642963/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150491/Bootstrap_Tests_How_Many_Bootstraps">Bootstrap Tests: How Many Bootstraps?</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Mar 1, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize experimental randomness. Simulation experiments suggest that this procedure will work very well in practice. This research was supported, in part, by grants from the Social Sciences and Humanities Research Council of Canada. 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</script> <div class="js-work-strip profile--work_container" data-work-id="115150441"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150441/Bootstrap_inference_in_a_linear_equation_estimated_by_instrumental_variables"><img alt="Research paper thumbnail of Bootstrap inference in a linear equation estimated by instrumental variables" class="work-thumbnail" src="https://attachments.academia-assets.com/111642928/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150441/Bootstrap_inference_in_a_linear_equation_estimated_by_instrumental_variables">Bootstrap inference in a linear equation estimated by instrumental variables</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Feb 1, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study several tests for the coefficient of the single right-hand-side endogenous variable in a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that all the test statistics-Student's t, Anderson-Rubin, Kleibergen's K, and likelihood ratio (LR)-can be written as functions of six random quantities. This leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and a conditional version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, K and conditional LR have excellent performance under the null, and LR also performs very well. However, power considerations suggest that the conditional LR test, bootstrapped using this new procedure when the sample size is not large, is probably the method of choice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4f63b8ea5b2c027dea2049f7552577cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642928,"asset_id":115150441,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642928/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150441"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150441"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150441; 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265594/The_Case_Against_Jive"><img alt="Research paper thumbnail of The Case Against Jive" class="work-thumbnail" src="https://attachments.academia-assets.com/108849037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265594/The_Case_Against_Jive">The Case Against Jive</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We perform an extensive series of Monte Carlo experiments to compare the performance of two varia...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We perform an extensive series of Monte Carlo experiments to compare the performance of two variants of the "Jackknife Instrumental Variables Estimator," or JIVE, with that of the more familiar 2SLS and LIML estimators. We find no evidence to suggest that JIVE should ever be used. It is always more dispersed than 2SLS, often very much so, and it is almost always inferior to LIML in all respects. Interestingly, JIVE seems to perform particularly badly when the instruments are weak.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f36b3bb58a1d722d65d69c3ea8f9998b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108849037,"asset_id":111265594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108849037/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265594; 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We show that all the test statistics-Student's t, Anderson-Rubin, Kleibergen's K, and likelihood ratio (LR)-can be written as functions of six random quantities. This leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and a conditional version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, K and conditional LR have excellent performance under the null, and LR also performs very well. However, power considerations suggest that the conditional LR test, bootstrapped using this new procedure when the sample size is not large, is probably the method of choice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ba60c3a5e12198a9eac81b089a5c7327" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848970,"asset_id":111265593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848970/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265593; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265592/Wild_Bootstrap_Tests_for_IV_Regression"><img alt="Research paper thumbnail of Wild Bootstrap Tests for IV Regression" class="work-thumbnail" src="https://attachments.academia-assets.com/108848968/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265592/Wild_Bootstrap_Tests_for_IV_Regression">Wild Bootstrap Tests for IV Regression</a></div><div class="wp-workCard_item"><span>Journal of Business & Economic Statistics</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a wild bootstrap procedure for linear regression models estimated by instrumental vari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="332e9360c8ed9bf81a2069d9bade5d18" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848968,"asset_id":111265592,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848968/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265592; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111265591/Time_and_Causality"><img alt="Research paper thumbnail of Time and Causality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111265591/Time_and_Causality">Time and Causality</a></div><div class="wp-workCard_item"><span>Annals of economics and statistics</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The understanding of causal chains and mechanisms is an essential part of any scientific activity...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The understanding of causal chains and mechanisms is an essential part of any scientific activity that aims at better explanation of its subject matter, and better understanding of it. While any account of causality requires that a cause should precede its effect, accounts of causality inphysics are complicated by the fact that the role of time in current theoretical physics has evolved very substantially throughout the twentieth century. In this article, I review the status of time and causality in physics, both the classical physics of the nineteenth century, and modern physics based on relativity and quantum mechanics. I then move on to econometrics, with some mention of statistics more generally, and emphasise the role of models in making sense of causal notions, and their place in scientific explanation</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111265591]").text(description); $(".js-view-count[data-work-id=111265591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111265591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111265591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111265591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111265591,"title":"Time and Causality","internal_url":"https://www.academia.edu/111265591/Time_and_Causality","owner_id":33782563,"coauthors_can_edit":true,"owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265590/Bootstrap_tests_how_many_bootstraps"><img alt="Research paper thumbnail of Bootstrap tests: how many bootstraps?" class="work-thumbnail" src="https://attachments.academia-assets.com/108848966/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265590/Bootstrap_tests_how_many_bootstraps">Bootstrap tests: how many bootstraps?</a></div><div class="wp-workCard_item"><span>Econometric Reviews</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea35144b0a2d0b1808511a8ac55caa53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848966,"asset_id":111265590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848966/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265590; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265589"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality"><img alt="Research paper thumbnail of Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality" class="work-thumbnail" src="https://attachments.academia-assets.com/108848964/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality">Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality</a></div><div class="wp-workCard_item"><span>Econometrica</span><span>, Nov 1, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We derive the asymptotic sampling distribution of various estimators frequently used to order dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8df8697a45bc65611ffd479d167b2629" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848964,"asset_id":111265589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848964/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265589; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111265589]").text(description); $(".js-view-count[data-work-id=111265589]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111265589; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111265589']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8df8697a45bc65611ffd479d167b2629" } } $('.js-work-strip[data-work-id=111265589]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111265589,"title":"Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality","translated_title":"","metadata":{"publisher":"Wiley-Blackwell","grobid_abstract":"We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.","publication_date":{"day":1,"month":11,"year":2000,"errors":{}},"publication_name":"Econometrica","grobid_abstract_attachment_id":108848964},"translated_abstract":null,"internal_url":"https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality","translated_internal_url":"","created_at":"2023-12-12T12:32:19.967-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33782563,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":108848964,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848964/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848964/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848964/lis-wps-181-libre.pdf?1702413258=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=BcaHmcENNF2ghds5TkppdScaRfGYUCk0D9O6imfrLNmmyiXAetleh27KfqBAXnMz7o0FNL4W2Sn0abj6lhLt5hjNkmSgLrJfhc~w0~W3~JFT7FSHO8s02N1C67opgibTELksTHvVSFNOvHJayaec2I0J1vh8fwt0yRghgm48s337hGJtMlKz3OkPHBbwCOJEa8yyvvvHc40RlvUMvY9WVj31F9z-EBAM2228CzXS9L1h8V5G5fR~UbZmAxQ9tuV3cHVHy7n0JiadHGUiz2zHWGjwszr8KBXjWs8XRhVrSIkygVUfY9BINv3AOp~7e1LOS1WVCTL56FvVtfQT71oZ4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality","translated_slug":"","page_count":41,"language":"en","content_type":"Work","summary":"We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.","owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson"},"attachments":[{"id":108848964,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848964/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848964/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848964/lis-wps-181-libre.pdf?1702413258=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=BcaHmcENNF2ghds5TkppdScaRfGYUCk0D9O6imfrLNmmyiXAetleh27KfqBAXnMz7o0FNL4W2Sn0abj6lhLt5hjNkmSgLrJfhc~w0~W3~JFT7FSHO8s02N1C67opgibTELksTHvVSFNOvHJayaec2I0J1vh8fwt0yRghgm48s337hGJtMlKz3OkPHBbwCOJEa8yyvvvHc40RlvUMvY9WVj31F9z-EBAM2228CzXS9L1h8V5G5fR~UbZmAxQ9tuV3cHVHy7n0JiadHGUiz2zHWGjwszr8KBXjWs8XRhVrSIkygVUfY9BINv3AOp~7e1LOS1WVCTL56FvVtfQT71oZ4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":108848963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848963/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848963/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848963/lis-wps-181-libre.pdf?1702413257=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=Z7O9Vl1hSxS9oOJRxzDNiQP2qLj2Ju5WdqYWZ6qTWN60VWR00pGcjIRYVryM3j02AXrsPp1loYL9WVlPkm7yEfjdyB2BFN~jKaDNboDentsNBtv58bUojwdTu5wm9sLQ1~DFg7iC5hXpYxQl6WB1gzEZQFRaCtlXKglrT3z5FTmLx6OEgqFtN1f7xnJntHH7jfpkUOO8LsKnIfHAAT8cOURwY62j~thYMQAnhSgbCUl4PX5ma~oKUrk~GTf3vPrg~CmUOH6mBuF9gDM4f-LfLXIzqWGss9Vw-2WjkjYvpcVZs-7SS6EvRYR6ItW4vWcDOjieEFqJ2OF4~T9Zu9QRAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing"},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics"},{"id":2808,"name":"Poverty","url":"https://www.academia.edu/Documents/in/Poverty"},{"id":6208,"name":"Economic Theory","url":"https://www.academia.edu/Documents/in/Economic_Theory"},{"id":20316,"name":"Stochastic dominance","url":"https://www.academia.edu/Documents/in/Stochastic_dominance"},{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference"},{"id":91070,"name":"Inequality","url":"https://www.academia.edu/Documents/in/Inequality"},{"id":806332,"name":"Estimator","url":"https://www.academia.edu/Documents/in/Estimator"},{"id":2555488,"name":"econometrica","url":"https://www.academia.edu/Documents/in/econometrica"}],"urls":[{"id":36971829,"url":"https://www.econstor.eu/bitstream/10419/160853/1/lis-wps-181.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265588/Moments_of_IV_and_JIVE_estimators"><img alt="Research paper thumbnail of Moments of IV and JIVE estimators" class="work-thumbnail" src="https://attachments.academia-assets.com/108849034/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265588/Moments_of_IV_and_JIVE_estimators">Moments of IV and JIVE estimators</a></div><div class="wp-workCard_item"><span>Econometrics Journal</span><span>, Oct 25, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We develop a method based on the use of polar coordinates to investigate the existence of moments...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We develop a method based on the use of polar coordinates to investigate the existence of moments for instrumental variables and related estimators in the linear regression model. For generalized IV estimators, we obtain familiar results. For JIVE, we obtain the new result that this estimator has no moments at all. Simulation results illustrate the consequences of its lack of moments. JEL codes: C100, C120, C300 This research was supported, in part, by grants from the Social Sciences and Humanities Research Council of Canada. We are grateful to two anonymous referees for comments on earlier versions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44d5cdab85b8ec82ecb55b9bda7e41cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108849034,"asset_id":111265588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108849034/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265588; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265587"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111265587/Tests_for_Model_Specification_in_the_Presence_of_Alternative_Hypotheses_Some_Further_Results"><img alt="Research paper thumbnail of Tests for Model Specification in the Presence of Alternative Hypotheses: Some Further Results" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111265587/Tests_for_Model_Specification_in_the_Presence_of_Alternative_Hypotheses_Some_Further_Results">Tests for Model Specification in the Presence of Alternative Hypotheses: Some Further Results</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, 1981</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In Davidson and MacKinnon (1981), two of the present authors proposed a novel and very s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract In Davidson and MacKinnon (1981), two of the present authors proposed a novel and very simple procedure for testing the specification of a nonlinear regression model against the evidence provided by a non-nested alternative. In this paper we extend their results in several directions. First, we relax a number of the assumptions of the previous paper; we admit the possibility that the nonlinear regression functions may depend on lagged dependent variables, and we do not require that the error terms be normally distributed. Second, we show how the earlier procedure may straightforwardly be generalized to the case where the two non-nested models involve different transformations of the dependent variable. Finally, we propose a simple procedure for testing non-nested linear regression models which have endogenous variables on the right-hand side, and have therefore been estimated by two-stage least squares.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265587"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265587"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265587; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111265587]").text(description); $(".js-view-count[data-work-id=111265587]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111265587; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111265587']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111265587]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111265587,"title":"Tests for Model Specification in the Presence of Alternative Hypotheses: Some Further Results","internal_url":"https://www.academia.edu/111265587/Tests_for_Model_Specification_in_the_Presence_of_Alternative_Hypotheses_Some_Further_Results","owner_id":33782563,"coauthors_can_edit":true,"owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson","email":"T2NoUjJ5TjFkL2FJN0dmMjZGdVY4NWZhMVgraG9xRkh3QkNMOXlpeGE0eFZPajZ6anZNV0FxQzRCMFl5b1hJRS0tVng2SEFLdDZsU0REMWE2V240OEZQdz09--b83691bb0336e25364e8d2577f1aa5756deb1fea"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3361421" id="papers"><div class="js-work-strip profile--work_container" data-work-id="125630183"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630183/Essays_in_comparative_dynamics"><img alt="Research paper thumbnail of Essays in comparative dynamics" class="work-thumbnail" src="https://attachments.academia-assets.com/119637608/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630183/Essays_in_comparative_dynamics">Essays in comparative dynamics</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the second section of the essay, it is assumed that some externality arises which adversely af...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the second section of the essay, it is assumed that some externality arises which adversely affects urban life and which provokes people to move out to suburbs. The consequences of this are studied and two different kinds of dynamical evolution can be distinguished. One, in which house construction in the suburbs is slow enough to make it necessary for new construction to continue in the city, tends not to be disastrous for the city; the other, in which all urban construction stops when the externality arises, usually leads to complete decay of the city. Throughout the thesis there is an emphasis on the differences in approach between static or quasistatic problems and dynamic ones.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="804215c2bceb4c440fe36d065c940e69" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637608,"asset_id":125630183,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637608/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630183"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630183"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630183; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630182"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630182/Asymptotic_and_bootstrap_inference_for_inequality_and_poverty_measures"><img alt="Research paper thumbnail of Asymptotic and bootstrap inference for inequality and poverty measures" class="work-thumbnail" src="https://attachments.academia-assets.com/119637607/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630182/Asymptotic_and_bootstrap_inference_for_inequality_and_poverty_measures">Asymptotic and bootstrap inference for inequality and poverty measures</a></div><div class="wp-workCard_item"><span>Journal of Econometrics</span><span>, Nov 1, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A random sample drawn from a population would appear to offer an ideal opportunity to use the boo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples, although inference with poverty indices is satisfactory. We find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e939241d493b5771a22877e710f328f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637607,"asset_id":125630182,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637607/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630182"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630182; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e939241d493b5771a22877e710f328f5" } } $('.js-work-strip[data-work-id=125630182]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125630182,"title":"Asymptotic and bootstrap inference for inequality and poverty measures","internal_url":"https://www.academia.edu/125630182/Asymptotic_and_bootstrap_inference_for_inequality_and_poverty_measures","owner_id":33782563,"coauthors_can_edit":true,"owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson","email":"WjVEZUhScWJhRDA0OVhPd3JHMm5WcjdDV3pKSXlXQmppYzBIZ1pKZURWQjhZUzk1RHo5aGlPY3NjNDY4bEpVRy0tSlIvcjZLSHpCQUlWM2dPM2VRQVh4Zz09--adcac82df7f982d8fdf5dda6d5e2bba22d987d53"},"attachments":[{"id":119637607,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119637607/thumbnails/1.jpg","file_name":"Davidson_Flachaire_04.pdf","download_url":"https://www.academia.edu/attachments/119637607/download_file","bulk_download_file_name":"Asymptotic_and_bootstrap_inference_for_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119637607/Davidson_Flachaire_04-libre.pdf?1731868106=\u0026response-content-disposition=attachment%3B+filename%3DAsymptotic_and_bootstrap_inference_for_i.pdf\u0026Expires=1740520485\u0026Signature=BSjRr23CrwErMGY33GB42twNbYg1v1Nt7PYrOwLg5hlickfyXMme07T1LJVLVn--l3Xmt~CElxnjxfGMv3FVeKIzI~QMOtQTzsFCQmXQKDSNTNm3Y1c3r1W-~WUaPqCRQwSZAJhrQMC0LRKqomh91HbKYmRrgjz958Oaekw71~F0iI~8h789B2-r4oy4pZUS20zVcTO7kZCs4T6nNIkECAte8jSX69enTwqlgW6qY914AsiGoSPqSyXP~pUExHl7Wv8zbKbi4zxlOBOwAFqJYZLKGqnM45VNGZgcBOvYofyj6GTomo~1Jbcu2kY35YerSCUyCpbL39Gl~4uuM3ks3A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":119637606,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119637606/thumbnails/1.jpg","file_name":"Davidson_Flachaire_04.pdf","download_url":"https://www.academia.edu/attachments/119637606/download_file","bulk_download_file_name":"Asymptotic_and_bootstrap_inference_for_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119637606/Davidson_Flachaire_04-libre.pdf?1731868100=\u0026response-content-disposition=attachment%3B+filename%3DAsymptotic_and_bootstrap_inference_for_i.pdf\u0026Expires=1740520485\u0026Signature=TpqwfI2l7md-TdBNpH2v8UxByqA0EGchM7pxbL8WJs8XiL6sMB-xWvodqrMQJU2XW4laoB9RsHhkPpggTdxtLFOOf63rzjqEXc89Llbji~XR4co3hdvuxTz9hHM8YoCjJZbQqCbkPgU0vp33zMxzOV~YrDKUcRloTGUO4AkrlWp9HPW5LtyPBwFXa563Ook8s8wTltOsOz03Nqov8EVFb5Q~HKsHInVxmOOILSHQT5gi65t2FEr~esx-frBUoBJWMtKUCLGvfd9oBy0dL9THF~PKYCErrncixRK529wddcGmspEbTRxf0EUuVe1QWm2Wh8vHqi1UMGfGXJh7uarnvg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630180"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630180/A_Parametric_Bootstrap_for_Heavy_Tailed_Distributions"><img alt="Research paper thumbnail of A Parametric Bootstrap for Heavy-Tailed Distributions" class="work-thumbnail" src="https://attachments.academia-assets.com/119637605/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630180/A_Parametric_Bootstrap_for_Heavy_Tailed_Distributions">A Parametric Bootstrap for Heavy-Tailed Distributions</a></div><div class="wp-workCard_item"><span>Econometric Theory</span><span>, Sep 8, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is known that Efron's resampling bootstrap of the mean of random variables with common distrib...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is known that Efron's resampling bootstrap of the mean of random variables with common distribution in the domain of attraction of the stable laws with infinite variance is not consistent, in the sense that the limiting distribution of the bootstrap mean is not the same as the limiting distribution of the mean from the real sample. Moreover, the limiting distribution of the bootstrap mean is random and unknown. The conventional remedy for this problem, at least asymptotically, is either the m out of n bootstrap or subsampling. However, we show that both these procedures can be quite unreliable in other than very large samples. A parametric bootstrap is derived by considering the distribution of the bootstrap P value instead of that of the bootstrap statistic. The quality of inference based on the parametric bootstrap is examined in a simulation study, and is found to be satisfactory with heavy-tailed distributions unless the tail index is close to 1 and the distribution is heavily skewed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="634578f32bbdaa6319946f83eb99550f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637605,"asset_id":125630180,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637605/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630180"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630180"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630180; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="125630168"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125630168/Testing_for_Restricted_Stochastic_Dominance"><img alt="Research paper thumbnail of Testing for Restricted Stochastic Dominance" class="work-thumbnail" src="https://attachments.academia-assets.com/119637597/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125630168/Testing_for_Restricted_Stochastic_Dominance">Testing for Restricted Stochastic Dominance</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance relation that we can seek to infer in many settings. One testing procedure that we consider is based on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the distributions under study that satisfy the null hypothesis, on the frontier between dominance and nondominance. These estimates can be used to perform dominance tests that can turn out to provide much improved reliability of inference compared with the asymptotic tests so far proposed in the literature.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44d550cfa820e23729eacc04d7e7807b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119637597,"asset_id":125630168,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119637597/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125630168"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125630168"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125630168; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123020299"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123020299/Testing_for_Restricted_Stochastic_Dominance"><img alt="Research paper thumbnail of Testing for Restricted Stochastic Dominance" class="work-thumbnail" src="https://attachments.academia-assets.com/117554062/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123020299/Testing_for_Restricted_Stochastic_Dominance">Testing for Restricted Stochastic Dominance</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to define and focus on restricted stochastic dominance, the only empirically useful form of dominance relation that we can seek to infer in many settings. One testing procedure that we consider is based on an empirical likelihood ratio. The computations necessary for obtaining a test statistic also provide estimates of the distributions under study that satisfy the null hypothesis, on the frontier between dominance and nondominance. These estimates can be used to perform dominance tests that can turn out to provide much improved reliability of inference compared with the asymptotic tests so far proposed in the literature.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4f6883cdf8bc4c051ba6a8ae884b450a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117554062,"asset_id":123020299,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117554062/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123020299"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123020299"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123020299; 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</script> <div class="js-work-strip profile--work_container" data-work-id="115150495"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150495/Wild_Bootstrap_Tests_for_IV_Regression"><img alt="Research paper thumbnail of Wild Bootstrap Tests for IV Regression" class="work-thumbnail" src="https://attachments.academia-assets.com/111642967/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150495/Wild_Bootstrap_Tests_for_IV_Regression">Wild Bootstrap Tests for IV Regression</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Aug 1, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a wild bootstrap procedure for linear regression models estimated by instrumental vari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1c35fc43ad6a906d88cd4c0b616af2f5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642967,"asset_id":115150495,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642967/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150495"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150495; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115150493"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150493/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality"><img alt="Research paper thumbnail of Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality" class="work-thumbnail" src="https://attachments.academia-assets.com/111642966/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150493/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality">Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Apr 1, 1998</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We derive the asymptotic sampling distribution of various estimators frequently used to order dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7802f5f1716695f0e9c7c5e6a650e9eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642966,"asset_id":115150493,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642966/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150493"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150493"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150493; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115150491"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150491/Bootstrap_Tests_How_Many_Bootstraps"><img alt="Research paper thumbnail of Bootstrap Tests: How Many Bootstraps?" class="work-thumbnail" src="https://attachments.academia-assets.com/111642963/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150491/Bootstrap_Tests_How_Many_Bootstraps">Bootstrap Tests: How Many Bootstraps?</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Mar 1, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize experimental randomness. Simulation experiments suggest that this procedure will work very well in practice. This research was supported, in part, by grants from the Social Sciences and Humanities Research Council of Canada. 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</script> <div class="js-work-strip profile--work_container" data-work-id="115150441"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115150441/Bootstrap_inference_in_a_linear_equation_estimated_by_instrumental_variables"><img alt="Research paper thumbnail of Bootstrap inference in a linear equation estimated by instrumental variables" class="work-thumbnail" src="https://attachments.academia-assets.com/111642928/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115150441/Bootstrap_inference_in_a_linear_equation_estimated_by_instrumental_variables">Bootstrap inference in a linear equation estimated by instrumental variables</a></div><div class="wp-workCard_item"><span>RePEc: Research Papers in Economics</span><span>, Feb 1, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study several tests for the coefficient of the single right-hand-side endogenous variable in a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that all the test statistics-Student's t, Anderson-Rubin, Kleibergen's K, and likelihood ratio (LR)-can be written as functions of six random quantities. This leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and a conditional version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, K and conditional LR have excellent performance under the null, and LR also performs very well. However, power considerations suggest that the conditional LR test, bootstrapped using this new procedure when the sample size is not large, is probably the method of choice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4f63b8ea5b2c027dea2049f7552577cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111642928,"asset_id":115150441,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111642928/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115150441"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115150441"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115150441; 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265594/The_Case_Against_Jive"><img alt="Research paper thumbnail of The Case Against Jive" class="work-thumbnail" src="https://attachments.academia-assets.com/108849037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265594/The_Case_Against_Jive">The Case Against Jive</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We perform an extensive series of Monte Carlo experiments to compare the performance of two varia...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We perform an extensive series of Monte Carlo experiments to compare the performance of two variants of the "Jackknife Instrumental Variables Estimator," or JIVE, with that of the more familiar 2SLS and LIML estimators. We find no evidence to suggest that JIVE should ever be used. It is always more dispersed than 2SLS, often very much so, and it is almost always inferior to LIML in all respects. Interestingly, JIVE seems to perform particularly badly when the instruments are weak.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f36b3bb58a1d722d65d69c3ea8f9998b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108849037,"asset_id":111265594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108849037/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265594; 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We show that all the test statistics-Student's t, Anderson-Rubin, Kleibergen's K, and likelihood ratio (LR)-can be written as functions of six random quantities. This leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and a conditional version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, K and conditional LR have excellent performance under the null, and LR also performs very well. However, power considerations suggest that the conditional LR test, bootstrapped using this new procedure when the sample size is not large, is probably the method of choice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ba60c3a5e12198a9eac81b089a5c7327" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848970,"asset_id":111265593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848970/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265593; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265592/Wild_Bootstrap_Tests_for_IV_Regression"><img alt="Research paper thumbnail of Wild Bootstrap Tests for IV Regression" class="work-thumbnail" src="https://attachments.academia-assets.com/108848968/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265592/Wild_Bootstrap_Tests_for_IV_Regression">Wild Bootstrap Tests for IV Regression</a></div><div class="wp-workCard_item"><span>Journal of Business & Economic Statistics</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We propose a wild bootstrap procedure for linear regression models estimated by instrumental vari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of unknown form. We apply this procedure to t tests, including heteroskedasticity-robust t tests, and to the Anderson-Rubin test. We provide simulation evidence that it works far better than older methods, such as the pairs bootstrap. We also show how to obtain reliable confidence intervals by inverting bootstrap tests. An empirical example illustrates the utility of these procedures.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="332e9360c8ed9bf81a2069d9bade5d18" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848968,"asset_id":111265592,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848968/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265592; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111265591/Time_and_Causality"><img alt="Research paper thumbnail of Time and Causality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111265591/Time_and_Causality">Time and Causality</a></div><div class="wp-workCard_item"><span>Annals of economics and statistics</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The understanding of causal chains and mechanisms is an essential part of any scientific activity...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The understanding of causal chains and mechanisms is an essential part of any scientific activity that aims at better explanation of its subject matter, and better understanding of it. While any account of causality requires that a cause should precede its effect, accounts of causality inphysics are complicated by the fact that the role of time in current theoretical physics has evolved very substantially throughout the twentieth century. In this article, I review the status of time and causality in physics, both the classical physics of the nineteenth century, and modern physics based on relativity and quantum mechanics. I then move on to econometrics, with some mention of statistics more generally, and emphasise the role of models in making sense of causal notions, and their place in scientific explanation</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111265591]").text(description); $(".js-view-count[data-work-id=111265591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111265591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111265591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111265591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111265591,"title":"Time and Causality","internal_url":"https://www.academia.edu/111265591/Time_and_Causality","owner_id":33782563,"coauthors_can_edit":true,"owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265590/Bootstrap_tests_how_many_bootstraps"><img alt="Research paper thumbnail of Bootstrap tests: how many bootstraps?" class="work-thumbnail" src="https://attachments.academia-assets.com/108848966/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265590/Bootstrap_tests_how_many_bootstraps">Bootstrap tests: how many bootstraps?</a></div><div class="wp-workCard_item"><span>Econometric Reviews</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea35144b0a2d0b1808511a8ac55caa53" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848966,"asset_id":111265590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848966/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265590; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265589"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality"><img alt="Research paper thumbnail of Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality" class="work-thumbnail" src="https://attachments.academia-assets.com/108848964/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality">Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality</a></div><div class="wp-workCard_item"><span>Econometrica</span><span>, Nov 1, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We derive the asymptotic sampling distribution of various estimators frequently used to order dis...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8df8697a45bc65611ffd479d167b2629" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108848964,"asset_id":111265589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108848964/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265589; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111265589]").text(description); $(".js-view-count[data-work-id=111265589]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111265589; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111265589']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8df8697a45bc65611ffd479d167b2629" } } $('.js-work-strip[data-work-id=111265589]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111265589,"title":"Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality","translated_title":"","metadata":{"publisher":"Wiley-Blackwell","grobid_abstract":"We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.","publication_date":{"day":1,"month":11,"year":2000,"errors":{}},"publication_name":"Econometrica","grobid_abstract_attachment_id":108848964},"translated_abstract":null,"internal_url":"https://www.academia.edu/111265589/Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality","translated_internal_url":"","created_at":"2023-12-12T12:32:19.967-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33782563,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":108848964,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848964/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848964/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848964/lis-wps-181-libre.pdf?1702413258=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=BcaHmcENNF2ghds5TkppdScaRfGYUCk0D9O6imfrLNmmyiXAetleh27KfqBAXnMz7o0FNL4W2Sn0abj6lhLt5hjNkmSgLrJfhc~w0~W3~JFT7FSHO8s02N1C67opgibTELksTHvVSFNOvHJayaec2I0J1vh8fwt0yRghgm48s337hGJtMlKz3OkPHBbwCOJEa8yyvvvHc40RlvUMvY9WVj31F9z-EBAM2228CzXS9L1h8V5G5fR~UbZmAxQ9tuV3cHVHy7n0JiadHGUiz2zHWGjwszr8KBXjWs8XRhVrSIkygVUfY9BINv3AOp~7e1LOS1WVCTL56FvVtfQT71oZ4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Statistical_Inference_for_Stochastic_Dominance_and_for_the_Measurement_of_Poverty_and_Inequality","translated_slug":"","page_count":41,"language":"en","content_type":"Work","summary":"We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, w elfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can beused to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines or income censoring thresholds up to which we may con dently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement o f p o v erty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are brie y illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases. On etudie les propri et es asymptotiques de plusieurs estimateurs fr equemment utilis es pour ordonner les r epartitions de revenus en termes de pauvret e, bien-être social et in egalit e. Ces estimateurs incluent les estimateurs de la plupart des indices de pauvret e couramment en usage ainsi que les estimateurs des courbes utiles pour l'inf erence de la dominance stochastique de n'importe quel ordre. Ces courbes nous permettent d e d eterminer si la pauvret e, l'in egalit e ou le bien-être social sont plus elev es dans une r epartition que dans une autre pour des classes g en erales d'indices. On etudie aussi la distribution echantillonnale des seuils maximum de pauvret e ou de censure des revenus jusqu'auxquels on peut a rmer sans ambigu t e que la pauvret e ou le bien-être social sont plus elev es dans une r epartition de revenus que dans une autre. La distribution echantillonnale d'estimateurs pour l'approche duale a la mesure de la pauvret e est aussi d eriv ee. Les r esultats statistiques s'appliquent a des seuils d eterministes ou stochastiques et a des echantillons d ependants ou ind ependants. On illustre bri evement nos r esultats a l'aide de donn ees sur 6 pays tir ees des banques de donn ees du Luxembourg Income Study.","owner":{"id":33782563,"first_name":"Russell","middle_initials":null,"last_name":"Davidson","page_name":"RussellDavidson","domain_name":"mcgill","created_at":"2015-08-10T07:28:19.700-07:00","display_name":"Russell Davidson","url":"https://mcgill.academia.edu/RussellDavidson"},"attachments":[{"id":108848964,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848964/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848964/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848964/lis-wps-181-libre.pdf?1702413258=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=BcaHmcENNF2ghds5TkppdScaRfGYUCk0D9O6imfrLNmmyiXAetleh27KfqBAXnMz7o0FNL4W2Sn0abj6lhLt5hjNkmSgLrJfhc~w0~W3~JFT7FSHO8s02N1C67opgibTELksTHvVSFNOvHJayaec2I0J1vh8fwt0yRghgm48s337hGJtMlKz3OkPHBbwCOJEa8yyvvvHc40RlvUMvY9WVj31F9z-EBAM2228CzXS9L1h8V5G5fR~UbZmAxQ9tuV3cHVHy7n0JiadHGUiz2zHWGjwszr8KBXjWs8XRhVrSIkygVUfY9BINv3AOp~7e1LOS1WVCTL56FvVtfQT71oZ4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":108848963,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/108848963/thumbnails/1.jpg","file_name":"lis-wps-181.pdf","download_url":"https://www.academia.edu/attachments/108848963/download_file","bulk_download_file_name":"Statistical_Inference_for_Stochastic_Dom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/108848963/lis-wps-181-libre.pdf?1702413257=\u0026response-content-disposition=attachment%3B+filename%3DStatistical_Inference_for_Stochastic_Dom.pdf\u0026Expires=1738615355\u0026Signature=Z7O9Vl1hSxS9oOJRxzDNiQP2qLj2Ju5WdqYWZ6qTWN60VWR00pGcjIRYVryM3j02AXrsPp1loYL9WVlPkm7yEfjdyB2BFN~jKaDNboDentsNBtv58bUojwdTu5wm9sLQ1~DFg7iC5hXpYxQl6WB1gzEZQFRaCtlXKglrT3z5FTmLx6OEgqFtN1f7xnJntHH7jfpkUOO8LsKnIfHAAT8cOURwY62j~thYMQAnhSgbCUl4PX5ma~oKUrk~GTf3vPrg~CmUOH6mBuF9gDM4f-LfLXIzqWGss9Vw-2WjkjYvpcVZs-7SS6EvRYR6ItW4vWcDOjieEFqJ2OF4~T9Zu9QRAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing"},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics"},{"id":2808,"name":"Poverty","url":"https://www.academia.edu/Documents/in/Poverty"},{"id":6208,"name":"Economic Theory","url":"https://www.academia.edu/Documents/in/Economic_Theory"},{"id":20316,"name":"Stochastic dominance","url":"https://www.academia.edu/Documents/in/Stochastic_dominance"},{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference"},{"id":91070,"name":"Inequality","url":"https://www.academia.edu/Documents/in/Inequality"},{"id":806332,"name":"Estimator","url":"https://www.academia.edu/Documents/in/Estimator"},{"id":2555488,"name":"econometrica","url":"https://www.academia.edu/Documents/in/econometrica"}],"urls":[{"id":36971829,"url":"https://www.econstor.eu/bitstream/10419/160853/1/lis-wps-181.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111265588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111265588/Moments_of_IV_and_JIVE_estimators"><img alt="Research paper thumbnail of Moments of IV and JIVE estimators" class="work-thumbnail" src="https://attachments.academia-assets.com/108849034/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111265588/Moments_of_IV_and_JIVE_estimators">Moments of IV and JIVE estimators</a></div><div class="wp-workCard_item"><span>Econometrics Journal</span><span>, Oct 25, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We develop a method based on the use of polar coordinates to investigate the existence of moments...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We develop a method based on the use of polar coordinates to investigate the existence of moments for instrumental variables and related estimators in the linear regression model. For generalized IV estimators, we obtain familiar results. For JIVE, we obtain the new result that this estimator has no moments at all. Simulation results illustrate the consequences of its lack of moments. JEL codes: C100, C120, C300 This research was supported, in part, by grants from the Social Sciences and Humanities Research Council of Canada. We are grateful to two anonymous referees for comments on earlier versions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44d5cdab85b8ec82ecb55b9bda7e41cf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108849034,"asset_id":111265588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108849034/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111265588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111265588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111265588; 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In this paper we extend their results in several directions. First, we relax a number of the assumptions of the previous paper; we admit the possibility that the nonlinear regression functions may depend on lagged dependent variables, and we do not require that the error terms be normally distributed. Second, we show how the earlier procedure may straightforwardly be generalized to the case where the two non-nested models involve different transformations of the dependent variable. 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