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Semiparametric Estimation Of Regression Models For Panel Data

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  • Joel L. Horowitz

    (Univ. of Iowa)

  • Marianthi Markatou

Abstract

Linear models with error components are widely used to analyze panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estimation of the densities of the error components, thereby avoiding the assumption that the densities belong to known parametric families. The nonparametric estimators are applied to an earnings model using data from the Current Population Survey. The model's transitory error component is not normally distributed. Use of the nonparametric density estimators yields estimates of the probability that individuals with low earnings will become high earners in the future that are much lower than the estimates obtained under the assumption of normally distributed error components. JEL Classification: C13, C14, C23

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 9309001.

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Length: 24 pages
Date of creation: 23 Sep 1993
Date of revision:
Handle: RePEc:wpa:wuwpem:9309001

Note: Zipped using PKZIP v2.04, encoded using UUENCODE v5.15. Zipped file includes 1 file -- UI93014.zip -- (body in WP5.1, 24 pages) (There are 4 tables and 5 graphs in Stata and Gauss format)
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  1. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
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