Semiparametric Estimation of Regression Models for Panel Data

Joel L. Horowitz, Marianthi Markatou

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

Linear models with error components are widely used to analyse 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.

Original languageEnglish (US)
Pages (from-to)145-168
Number of pages24
JournalReview of Economic Studies
Volume63
Issue number1
DOIs
StatePublished - 1996

ASJC Scopus subject areas

  • Economics and Econometrics

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