Improved errors-in-variables estimators for grouped data

Paul Devereux

No 200602, Working Papers from School of Economics, University College Dublin

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.

Keywords: Psuedo-panel; Small sample bias; Labor supply; Labor supply--Mathematical models; Jackknife (Statistics); Monte Carlo method (search for similar items in EconPapers)
Date: 2006-01
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Citations: View citations in EconPapers (1)

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http://hdl.handle.net/10197/748 First version, 2006 (application/pdf)

Related works:
Journal Article: Improved Errors-in-Variables Estimators for Grouped Data (2007)
Working Paper: Improved Errors-in-Variables Estimators for Grouped Data (2007)
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