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BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS

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  • Kim, Min Seong
  • Sun, Yixiao

Abstract

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n, the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k-step bootstrap to correct the bias. We establish the asymptotic validity of the bootstrap bias corrections for both model parameters and average marginal effects. Our results apply to static models as well as some dynamic Markov models. Monte Carlo simulations show that our procedures are effective in reducing the bias of the fixed effects estimator and improving the coverage accuracy of the associated confidence interval.

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  • Kim, Min Seong & Sun, Yixiao, 2016. "BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS," Econometric Theory, Cambridge University Press, vol. 32(6), pages 1523-1568, December.
    • Handle: RePEc:cup:etheor:v:32:y:2016:i:06:p:1523-1568_00
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    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Martin Weidner, 2018. "Network and panel quantile effects via distribution regression," CeMMAP working papers CWP21/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
    3. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.
    4. Jochmans, Koen & Higgins, Ayden, 2022. "Bootstrap inference for fixed-effect models," TSE Working Papers 22-1328, Toulouse School of Economics (TSE), revised Dec 2023.
    5. Dhaene, Geert & Sun, Yutao, 2021. "Second-order corrected likelihood for nonlinear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 220(2), pages 227-252.
    6. Maeregu W. Arisido & Fulvia Mecatti & Paola Rebora, 2022. "Improving the causal treatment effect estimation with propensity scores by the bootstrap," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 455-471, September.
    7. Andreas Dzemski, 2019. "An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 763-776, December.
    8. William C. Horrace & Hyunseok Jung & Shane Sanders, 2022. "Network Competition and Team Chemistry in the NBA," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 35-49, January.
    9. Shuowen Chen, 2022. "Indirect Inference for Nonlinear Panel Models with Fixed Effects," Papers 2203.10683, arXiv.org, revised Apr 2022.

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