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Semiparametric estimation of dynamic discrete choice models

Buchholz, Nicholas and Shum, Matthew and Xu, Haiqing (2021) Semiparametric estimation of dynamic discrete choice models. Journal of Econometrics, 223 (2). pp. 312-327. ISSN 0304-4076. doi:10.1016/j.jeconom.2020.01.024.

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We consider the estimation of dynamic binary choice models in a semiparametric setting, in which the per-period utility functions are known up to a finite number of parameters, but the distribution of utility shocks is left unspecified. This semiparametric setup differs from most of the existing identification and estimation literature for dynamic discrete choice models. To show identification we derive and exploit a new recursive representation for the unknown quantile function of the utility shocks. Our estimators are straightforward to compute, and resemble classic closed-form estimators from the literature on semiparametric regression and average derivative estimation. Monte Carlo simulations demonstrate that our estimator performs well in small samples.

Item Type:Article
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Shum, Matthew0000-0002-6262-915X
Additional Information:© 2020 Elsevier. Received 21 April 2018, Revised 29 January 2020, Accepted 29 January 2020, Available online 18 September 2020. We thank Hassan Afrouzi, Saroj Bhattarai, Stephane Bonhomme, Denis Chetverikov, Kirill Pogorelskiy, Eduardo Souza-Rodrigues, Tang Srisuma, and seminar participants at Academia Sinica, Arizona, Carnegie-Mellon, Chicago, Colorado (Boulder), Duke, Einaudi Institute, Irvine, Washington (Seattle), UT Austin, Wisconsin, Yale, and Texas Metrics Camp for helpful discussions.
Subject Keywords:Semiparametric estimation; Dynamic discrete choice model; Average derivative estimation; Fredholm integral operators
Issue or Number:2
Classification Code:JEL classification C14; D91; C41; L91
Record Number:CaltechAUTHORS:20210626-183440558
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Official Citation:Nicholas Buchholz, Matthew Shum, Haiqing Xu, Semiparametric estimation of dynamic discrete choice models, Journal of Econometrics, Volume 223, Issue 2, 2021, Pages 312-327, ISSN 0304-4076,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109605
Deposited By: George Porter
Deposited On:28 Jun 2021 22:46
Last Modified:16 Nov 2021 19:37

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