Improved errors-in-variables estimators for grouped data

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Show simple item record Devereux, Paul J. 2008-12-12T15:13:08Z 2008-12-12T15:13:08Z UCD School of Economics 2006 en 2006-01
dc.identifier.other 200602 en
dc.description.abstract Grouping models are widely used in economics but are subject to finite sample bias. I show that the standard errors-in-variables estimator (EVE) is exactly equivalent to the Jackknife Instrumental Variables Estimator (JIVE), and use this relationship to develop an estimator which, unlike EVE, is unbiased in finite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor supply using microdata from the United States Census. There are sizeable differences in the wage elasticity across estimators, showing the practical importance of the theoretical issues even when the sample size is quite large. en
dc.format.extent 266167 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher University College Dublin. School of Economics en
dc.relation.ispartofseries UCD Centre for Economic Research Working Paper Series en
dc.relation.ispartofseries WP06/02 en
dc.subject Psuedo-panel en
dc.subject Small sample bias en
dc.subject Labor supply en
dc.subject.lcsh Labor supply--Mathematical models en
dc.subject.lcsh Jackknife (Statistics) en
dc.subject.lcsh Monte Carlo method en
dc.title Improved errors-in-variables estimators for grouped data en
dc.type Working Paper en
dc.internal.authorurl Paul J. Devereux (web page) en
dc.internal.authorurl en
dc.internal.authorid UCD0006 en
dc.internal.availability Full text available en
dc.internal.webversions en
dc.status Not peer reviewed en
dc.neeo.contributor Devereux|Paul J.|aut|UCD0006

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