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

Do Won Kwak (), Robert Martin and Jeffrey Wooldridge

Journal of Econometric Methods, 2023, vol. 12, issue 1, 33-56

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.

Keywords: panel data; binary dependent variable; conditional logit model; unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C15 C23 C25 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation (2018)
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DOI: 10.1515/jem-2021-0005

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