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Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects

Author

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  • Ivan Fernandez-Val

    () (Department of Economics, Boston University)

Abstract

Fixed e®ects estimates of structural parameters in nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper I show that the most important com- ponent of this incidental parameters bias for probit ¯xed e®ects estimators of index coe±cients is proportional to the true parameter value, using a large-T expansion of the bias. This result allows me to derive a lower bound for this bias, and to show that ¯xed e®ects estimates of ratios of coe±cients and average marginal e®ects have zero bias in the absence of heterogeneity and have negligible bias relative to their true values for a wide range of distributions of regressors and individual e®ects. Numerical examples suggest that this small bias property also holds for logit and linear probability models, and for exogenous variables in dynamic binary choice models. An empirical analysis of female labor force participation using data from the PSID shows that whereas the signi¯cant biases in ¯xed e®ects estimates of model parameters do not contami- nate the estimates of marginal e®ects in static models, estimates of both index coe±cients and marginal e®ects can be severely biased in dynamic models. Improved bias corrected estimators for index coe±cients and marginal e®ects are also proposed for both static and dynamic models.

Suggested Citation

  • 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.
  • Handle: RePEc:bos:wpaper:wp2005-38
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    References listed on IDEAS

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    Cited by:

    1. Kruger, Diana I., 2007. "Coffee production effects on child labor and schooling in rural Brazil," Journal of Development Economics, Elsevier, vol. 82(2), pages 448-463, March.
    2. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    3. 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.
    4. Manuel Arellano & Jinyong Hahn, 2005. "Understanding Bias In Nonlinear Panel Models: Some Recent Developments," Working Papers wp2005_0507, CEMFI.
    5. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.
    6. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, 04.
    7. Nini, Greg & Smith, David C. & Sufi, Amir, 2009. "Creditor control rights and firm investment policy," Journal of Financial Economics, Elsevier, vol. 92(3), pages 400-420, June.
    8. Inmaculada García & José Molina & María Navarro, 2007. "How Satisfied are Spouses with their Leisure Time? Evidence from Europe," Journal of Family and Economic Issues, Springer, vol. 28(4), pages 546-565, December.
    9. Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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