{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:01:24Z","timestamp":1780588884001,"version":"3.54.1"},"reference-count":58,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2019 export decisions, the informativeness of the identified sets, and we assess the impact of (common) model restrictions on results.<\/jats:p>","DOI":"10.1093\/restud\/rdag039","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T11:47:26Z","timestamp":1778240846000},"source":"Crossref","is-referenced-by-count":0,"title":["Counterfactual Analysis for Structural Dynamic Discrete Choice Models"],"prefix":"10.1093","author":[{"given":"Myrto","family":"Kalouptsidi","sequence":"first","affiliation":[{"name":"Harvard University, USA,\u00a0CEPR, UK,\u00a0and NBER, USA Department of Economics,\u00a0"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Kitamura","sequence":"additional","affiliation":[{"name":"Yale University, USA Department of Economics,\u00a0"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Lima","sequence":"additional","affiliation":[{"name":"PUC-Rio Department of Economics,\u00a0 , Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Souza-Rodrigues","sequence":"additional","affiliation":[{"name":"University of Toronto, Canada,\u00a0and CEPR Department of Economics,\u00a0 , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"2026060411170304200_rdag039-B1","first-page":"42","article-title":"Mutually Consistent Revealed Preference Demand Predictions","volume":"12","author":"Adams","year":"2020","journal-title":"American Economic Journal: Microeconomics"},{"key":"2026060411170304200_rdag039-B2","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1257\/aer.20150956","article-title":"Nonparametric Counterfactual Predictions in Neoclassical 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