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Bias Correction in Panel Data Models with Individual Specific Parameters

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  • Ivan Fernandez-Val

    () (Department of Economics, Boston University)

Abstract

In random coefficients linear IV models, fixed effects averages of the random coefficients are biased in short panels due to the finite-sample bias of IV estimators. This paper introduces a new class of bias-corrected fixed effects estimators for panel data models where the response to the regressors can be individual-specific in an unrestricted fashion. These estimators are based on moment conditions that can be nonlinear functions in parameters and variables, encompassing both linear and nonlinear random coe±cients models and allowing for the presence of endogenous regressors. The corrections are derived from large-T expansions of the finite-sample bias, and reduce the order of this bias from O(T¡1) to O(T¡2) for model parameters and other quantities of interest, such as averages of the individual-specific coefficients. The asymptotic distribution of the bias-corrected estimators are centered at the true parameter values under asymptotic sequences where n = o(T3). In a Monte Carlo example for a linear IV model with both common and individual-specific coefficients, I find that estimators that do not account for parameter heterogeneity can be severely biased, and that bias corrections are effective in reducing the bias of fixed effects estimates. These methods are illustrated through an analysis of earnings equations for young men allowing the effect of the union status to be different for each individual. The results suggest the presence of important heterogeneity in the union premium.

Suggested Citation

  • Ivan Fernandez-Val, 2005. "Bias Correction in Panel Data Models with Individual Specific Parameters," Boston University - Department of Economics - Working Papers Series WP2005-041, Boston University - Department of Economics.
  • Handle: RePEc:bos:wpaper:wp2005-041
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    References listed on IDEAS

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    1. repec:ubc:bricol:90-03 is not listed on IDEAS
    2. Jawwad Noor, 2005. "Temptation, Welfare and Revealed Preference," Boston University - Department of Economics - Working Papers Series WP2005-15, Boston University - Department of Economics.
    3. Eddie eckel & Barton L Lipman & Aldo Rustichini & Todd Sarver, 2005. "Representing Preferences with a Unique Subjective State Space: Corrigendum," Boston University - Department of Economics - Working Papers Series WP2005-042, Boston University - Department of Economics.
    4. Kopylov Igor, 2009. "Temptations in General Settings," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 9(1), pages 1-25, September.
    5. Faruk Gul & Wolfgang Pesendorfer, 2001. "Temptation and Self-Control," Econometrica, Econometric Society, vol. 69(6), pages 1403-1435, November.
    6. Dekel, Eddie & Lipman, Barton L & Rustichini, Aldo, 2001. "Representing Preferences with a Unique Subjective State Space," Econometrica, Econometric Society, vol. 69(4), pages 891-934, July.
    7. Machina, Mark J, 1989. "Dynamic Consistency and Non-expected Utility Models of Choice under Uncertainty," Journal of Economic Literature, American Economic Association, vol. 27(4), pages 1622-1668, December.
    8. John C. Harsanyi, 1955. "Cardinal Welfare, Individualistic Ethics, and Interpersonal Comparisons of Utility," Journal of Political Economy, University of Chicago Press, vol. 63, pages 309-309.
    9. Todd Sarver, 2008. "Anticipating Regret: Why Fewer Options May Be Better," Econometrica, Econometric Society, vol. 76(2), pages 263-305, March.
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    Cited by:

    1. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
    2. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    3. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.
    4. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    5. Ivan Fernandez-Val, 2005. "Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects," Boston University - Department of Economics - Working Papers Series WP2005-38, Boston University - Department of Economics.

    More about this item

    Keywords

    Random Coefficients; Panel Data; GMM; Fixed Effects; Bias; Union Premium.;

    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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