Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference

Ivan Fernandez-Val () and Joonhwan Lee ()
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Joonhwan Lee: MIT

No wp2010-001, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: The main purpose of this paper is to estimate panel data models with endogenous regressors and nonadditive unobserved individual heterogeneity including, for example, linear and nonlinear models where all the parameters can vary across individuals. The quantities of interest are means, variances, and other moments of the individual parameters. Since estimates of these quantities based on individual by individual GMM estimation can be severely biased due to the incidental parameter problem, we develop bias corrections that give more accurate estimates in moderately long panels. These corrections, derived from large-T expansions of the finite-sample bias of fixed effects GMM estimators, reduce the order of the bias from O(T¡1) to O(T¡2) and center the asymptotic distributions at the true values in moderately long panels under asymptotic sequences where n = o(T3). An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.

Keywords: Correlated Random Coefficient Model; Panel Data; Instrumental Variables; GMM; Fixed Effects; Bias; Cigarette demand (search for similar items in EconPapers)
JEL-codes: C23 J31 J51 (search for similar items in EconPapers)
Pages: 77
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Related works:
Journal Article: Panel data models with nonadditive unobserved heterogeneity: Estimation and inference (2013)
Working Paper: Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference (2013)
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