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SEMIPARAMETRIC ESTIMATION OF DYNAMIC BINARY CHOICE PANEL DATA MODELS

Published online by Cambridge University Press:  11 March 2024

Fu Ouyang Open the ORCID record for Fu Ouyang [Opens in a new window]  and
Thomas Tao Yang Open the ORCID record for Thomas Tao Yang [Opens in a new window]
Fu Ouyang*
Affiliation:
University of Queensland
Thomas Tao Yang
Affiliation:
Australian National University
*
Address correspondence to Fu Ouyang, School of Economics, University of Queensland, St Lucia, QLD, Australia, e-mail: f.ouyang@uq.edu.au
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Abstract

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We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model under consideration has the same random utility framework as in Honoré and Kyriazidou (2000, Econometrica 68, 839–874). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model can proceed in two steps, and requires matching only the value of an index function of explanatory variables over time, rather than the value of each explanatory variable. Our identification method motivates an easily implementable, two-step maximum score (2SMS) procedure – producing estimators whose rates of convergence, in contrast to Honoré and Kyriazidou’s (2000, Econometrica 68, 839–874) methods, are independent of the model dimension. We then analyze the asymptotic properties of the 2SMS procedure and propose bootstrap-based distributional approximations for inference. Evidence from Monte Carlo simulations indicates that our procedure performs satisfactorily in finite samples.

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ARTICLES
Information
Econometric Theory , Volume 41 , Issue 4 , August 2025 , pp. 907 - 946
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

We thank the editor (Peter C. B. Phillips), the co-editor (Iván Fernández-Val), and the two anonymous referees for their valuable comments and suggestions, which have significantly improved the quality of this paper. We are grateful to Yonghong An, Shakeeb Khan, Arthur Lewbel, Takuya Ura, Hanghui Zhang, and Yichong Zhang for their insightful feedback and discussions. We also thank participants at the 2019 Asian Meeting of the Econometric Society, the 2019 Shanghai Workshop of Econometrics, and the 2022 Australasia Meeting of the Econometric Society for their helpful comments. Fu Ouyang acknowledges the financial support provided by the Faculty of Business, Economics, and Law (BEL) at the University of Queensland through the 2019 BEL New Staff Research Start-Up Grant. All errors are our responsibility.

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