Identification and Estimation of Nonstationary Dynamic Binary Choice Models

Cheng Chou (), Geert Ridder () and Ruoyao Shi ()
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Cheng Chou: University of Leicester
Geert Ridder: University of Southern California
Ruoyao Shi: Department of Economics, University of California Riverside

No 202402, Working Papers from University of California at Riverside, Department of Economics

Abstract: In a dynamic binary choice model that allows for general forms of nonstationarity, we transform the identification of the flow utility parameters into the solution of a (linear) system of equations. The identification of the parameters, therefore, follows the usual argument for linear GMM. In particular, we show that the state transition distribution is not essential for the identification and estimation of the parameters. We propose a three-step conditional-choice-probability-based semiparametric estimator that bypasses estimation of and simulating from the state transition distribution. Simulation experiments show that our estimator gives comparable or better estimates than a competitor estimator, yet it requires fewer assumptions in certain scenarios, is substantially easier to implement, and is computationally much less demanding. The asymptotic distribution of the estimator is provided, and the sensitivity of the estimator to a key assumption is also examined.

Keywords: dynamic binary choice model; Markov property; linear system; identification; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C35 (search for similar items in EconPapers)
Pages: 55 Pages
Date: 2024-02
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-upt
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