Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification

58 Pages Posted: 18 Feb 2021

See all articles by Yuya Sasaki

Yuya Sasaki

Vanderbilt University - College of Arts and Science - Department of Economics

Yuya Takahashi

University of Washington

Yi Xin

California Institute of Technology

Yingyao Hu

Johns Hopkins University - Department of Economics

Date Written: December 17, 2021

Abstract

In many empirical studies, the states that are relevant for forward-looking economic agents to make decisions may not be included in the data to which researchers have ac- cess. This problem often arises in the context of declining/booming industries. In this paper, we develop the sharp identified sets of structural parameters and counterfactuals for dynamic discrete choice models when empirical data do not cover realizations of relevant future states. Applying the proposed method to the annual Toyo Keizai database, we study the behaviors of Japanese firms on foreign direct investments in China without observing the future states after Chinese economy slows down.

Keywords: dynamic discrete choice, incomplete data, industry dynamics, partial identification, sharp identification

JEL Classification: C18

Suggested Citation

Sasaki, Yuya and Takahashi, Yuya and Xin, Yi and Hu, Yingyao, Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification (January 14, 2021). Available at SSRN: https://ssrn.com/abstract=3766380 or http://dx.doi.org/10.2139/ssrn.3766380

Yuya Sasaki

Vanderbilt University - College of Arts and Science - Department of Economics ( email )

Box 1819 Station B
Nashville, TN 37235
United States

Yuya Takahashi (Contact Author)

University of Washington ( email )

Seattle, WA 98195
United States

HOME PAGE: http://https://econ.washington.edu/people/yuya-takahashi

Yi Xin

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Yingyao Hu

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

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