Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification
58 Pages Posted: 18 Feb 2021 Last revised: 18 Jan 2022
Date Written: December 17, 2021
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
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