Partial effects estimation for fixed-effects logit panel data models

Francesco Bartolucci and Claudia Pigini

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a multiple step procedure to estimate Average Partial Effects (APE) in fixed-effects panel logit models. Because the incidental parameters problem plagues the APEs via both the inconsistent estimates of the slope and individual parameters, we reduce the bias by evaluating the APEs at a fixed-T consistent estimator for the slope coefficients and at a bias corrected estimator for the unobserved heterogeneity. The proposed estimator has bias of order O(T −2 ) as n → ∞ and performs well in finite sample, even when n is much larger than T . We provide a real data application based on the labor supply of married women.

Keywords: Average partial effects; Bias reduction; Binary panel data; Conditional Maximum Likelihood (search for similar items in EconPapers)
JEL-codes: C12 C23 C25 (search for similar items in EconPapers)
Date: 2019-02-18
New Economics Papers: this item is included in nep-dcm and nep-ore
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Citations: View citations in EconPapers (3)

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https://mpra.ub.uni-muenchen.de/92243/1/MPRA_paper_92243.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/92251/5/MPRA_paper_92251.pdf revised version (application/pdf)

Related works:
Working Paper: Partial effects estimation for fixed-effects logit panel data models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:92243

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