Dynamic discrete choice models with incomplete data: Sharp identification

A-Tier
Journal: Journal of Econometrics
Year: 2023
Volume: 236
Issue: 1

Authors (4)

Sasaki, Yuya (Vanderbilt University) Takahashi, Yuya (not in RePEc) Xin, Yi (not in RePEc) Hu, Yingyao (not in RePEc)

Score contribution per author:

1.005 = (α=2.01 / 4 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

In many empirical studies, those states that are relevant for forward-looking economic agents to make decisions may not be included in the data to which researchers have access. 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.

Technical Details

RePEc Handle
repec:eee:econom:v:236:y:2023:i:1:s0304407623001550
Journal Field
Econometrics
Author Count
4
Added to Database
2026-01-29