Counterfactual Analysis for Structural Dynamic Discrete Choice Models

• 成果类型: Article; Early Access
• 署名作者: Kalouptsidi, Myrto; Kitamura, Yuichi; Lima, Lucas; Souza-Rodrigues, Eduardo
• 署名单位: Harvard University; Centre for Economic Policy Research - UK; National Bureau of Economic Research; Yale University; Pontificia Universidade Catolica do Rio de Janeiro; University of Toronto
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdag039
• 发表日期: 2026
• 关键词: Nonparametric identification; Market entry; inference; estimators; parameters

摘要

Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. In fact, in the case of dynamic discrete choice models, only a narrow set of counterfactuals are point-identified. In this paper, we explore how much one can learn about counterfactual outcomes of interest within this framework. We focus on the partial identification of counterfactuals, while allowing for (mild) model restrictions that can gradually shrink the identified set. We derive bounds for low-dimensional objects (such as average welfare) as arguments of optimization programmes, along with a uniformly valid inference procedure. Furthermore, we develop new and tractable computational tools and algorithms suitable for dealing with high-dimensional problems like this. Finally, we illustrate in Monte Carlos, as well as an empirical exercise of firms' export decisions, the informativeness of the identified sets, and we assess the impact of (common) model restrictions on results.