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The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

Author

Listed:
  • Kwak Do Won

    (Graduate School of International Studies, Korea University, Seoul, 02841, Korea)

  • Martin Robert S.

    (Division of Price and Index Number Research, Bureau of Labor Statistics, 2 Massachusetts Ave, NE, Washington, DC, 20212, USA)

  • Wooldridge Jeffrey M.

    (Department of Economics, Michigan State University, East Lansing, MI 48824-1038, USA)

Abstract

We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

Suggested Citation

  • Kwak Do Won & Martin Robert S. & Wooldridge Jeffrey M., 2023. "The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 33-56, January.
    • Handle: RePEc:bpj:jecome:v:12:y:2023:i:1:p:33-56:n:4
    DOI: 10.1515/jem-2021-0005
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    Keywords

    panel data; binary dependent variable; conditional logit model; unobserved heterogeneity; C15; C23; C25;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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

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