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

Authors
Kwak, D.W.Martin, R.S.Wooldridge, J.M.
Issue Date
2021
Publisher
De Gruyter Open Ltd
Keywords
binary dependent variable; conditional logit model; panel data; unobserved heterogeneity
Citation
Journal of Econometric Methods
Indexed
SCOPUS
Journal Title
Journal of Econometric Methods
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138453
DOI
10.1515/jem-2021-0005
ISSN
2156-6674
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. © 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.
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