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Review of Economic Studies 2026

Counterfactual Analysis for Structural Dynamic Discrete Choice Models

Myrto Kalouptsidi1; Yuichi Kitamura2; Lucas Lima3; Eduardo Souza-Rodrigues4

1 Harvard University, USA, CEPR, UK, and NBER, USA Department of Economics, · 2 Yale University, USA Department of Economics, · 3 PUC-Rio Department of Economics,  , Brazil · 4 University of Toronto, Canada, and CEPR Department of Economics,  , UK

open access

Abstract

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.

DOI
10.1093/restud/rdag039
Language
en
Sources
openalex crossref

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