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

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
JournalJournal of Econometric Methods
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • binary dependent variable
  • conditional logit model
  • panel data
  • unobserved heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Applied Mathematics

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