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Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference

Author

Listed:
  • Iván Fernández-Val

    (Boston University, Department of Economics)

  • Joonhwan Lee

    (MIT)

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.

Suggested Citation

  • Iván Fernández-Val & Joonhwan Lee, "undated". "Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference," Boston University - Department of Economics - Working Papers Series wp2010-001, Boston University - Department of Economics.
    • Handle: RePEc:bos:wpaper:wp2010-001
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J51 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Trade Unions: Objectives, Structure, and Effects

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