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

Author

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  • Do Won Kwak
  • Robert S. Martin
  • Jeffrey M. Wooldridge

Abstract

This paper examines 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 and pooled correlated random effects logit.

Suggested Citation

  • Do Won Kwak & Robert S. Martin & Jeffrey M. Wooldridge, 2018. "The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation," Economic Working Papers 502, Bureau of Labor Statistics.
    • Handle: RePEc:bls:wpaper:502
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    File URL: https://www.bls.gov/osmr/research-papers/2018/pdf/ec180020.pdf
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    Cited by:

    1. Paola Conconi & Florin Cucu & Federico Gallina & Mattia Nordotto, 2024. "A political disconnect? Evidence from voting on EU trade agreements," CEP Discussion Papers dp2041, Centre for Economic Performance, LSE.
    2. Rosmer, Paul, 2024. "A Comment on "Preference Discovery in University Admissions: The Case for Dynamic Multioffer Mechanisms"," I4R Discussion Paper Series 166, The Institute for Replication (I4R).

    More about this item

    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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