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Bias Corrections for Two-Step Fixed Effects Panel Data Estimators

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  • Fernández-Val, Iván

    () (Boston University)

  • Vella, Francis

    () (Georgetown University)

Abstract

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time variant heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control function from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control function accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two step nature of the procedure. As these two step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.

Suggested Citation

  • Fernández-Val, Iván & Vella, Francis, 2007. "Bias Corrections for Two-Step Fixed Effects Panel Data Estimators," IZA Discussion Papers 2690, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp2690
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    Cited by:

    1. Fernández-Val, Iván & Weidner, Martin, 2016. "Individual and time effects in nonlinear panel models with large N, T," Journal of Econometrics, Elsevier, vol. 192(1), pages 291-312.
    2. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    3. Ramon Cobo-Reyes & Gabriel Katz & Simone Meraglia?, 2017. "Endogenous Sanctioning Institutions and Migration Patterns: Experimental Evidence," Discussion Papers 1702, University of Exeter, Department of Economics.
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    5. Panutat Satchachai & Peter Schmidt, 2010. "Estimates of technical inefficiency in stochastic frontier models with panel data: generalized panel jackknife estimation," Journal of Productivity Analysis, Springer, vol. 34(2), pages 83-97, October.
    6. Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Jun 2018.
    7. Elizabeth Schroeder, 2016. "Dynamic labor supply adjustment with bias correction," Empirical Economics, Springer, vol. 51(4), pages 1623-1640, December.
    8. Chen, Mingli, 2016. "Estimation of Nonlinear Panel Models with Multiple Unobserved Effects," Economic Research Papers 269326, University of Warwick - Department of Economics.
    9. Harrison Fell & Daniel T. Kaffine, 2014. "A one-two punch: Joint effects of natural gas abundance and renewables on coal-fired power plants," Working Papers 2014-10, Colorado School of Mines, Division of Economics and Business.
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    13. Chrysanthou, Georgios Marios, 2008. "Estimating union wage effects in Great Britain during 1991-2003," UC3M Working papers. Economics we082214, Universidad Carlos III de Madrid. Departamento de Economía.
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    15. Aysen Isaoglu, 2010. "Worker Reallocation across Occupations in Western Germany," SOEPpapers on Multidisciplinary Panel Data Research 319, DIW Berlin, The German Socio-Economic Panel (SOEP).
    16. Michael J. Hicks & Michael LaFaive & Srikant Devaraj, 2016. "New Evidence on the Effect of Right-to-Work Laws on Productivity and Population Growth," Cato Journal, Cato Journal, Cato Institute, vol. 36(1), pages 101-120, Winter.
    17. Chrysanthou, Georgios Marios, 2014. "Heterogeneity, Endogeneity, Measurement Error and Identification of the Union Wage Impact," QM&ET Working Papers 14-4, University of Alicante, D. Quantitative Methods and Economic Theory, revised 05 Nov 2014.
    18. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Vienna University of Technology Working Papers in Economic Theory and Policy 07/2017, Vienna University of Technology, Institute for Mathematical Methods in Economics, Research Group Economics (ECON).
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    More about this item

    Keywords

    bias; fixed effects; endogenous regressors; two-step estimation; panel data; 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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