Counterfactual Analysis for Structural Dynamic Discrete Choice Models
Abstract/Contents
- Abstract
- Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. This fundamental tension is important for dynamic discrete choice models because agents' behavior depends on value functions, which require utilities in levels. We propose a unified approach to investigate how much one can learn about counterfactual outcomes under mild assumptions, for a large and empirically relevant class of counterfactuals. We derive analytical properties of sharp identified sets under alternative model restrictions and develop a valid inference approach based on subsampling. To aid practitioners, we propose computationally tractable procedures that bypass model estimation and directly obtain the identified sets for the counterfactuals and the corresponding confidence sets. 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.
Description
| Type of resource | text |
|---|---|
| Date created | July 12, 2021 |
Creators/Contributors
| Author | Kalouptsidi, Myrto |
|---|---|
| Author | Kitamura, Yuichi |
| Author | Lima, Lucas |
| Author | Souza-Rodrigues, Eduardo |
| Organizer of meeting | Santos, Andres |
| Organizer of meeting | Shaikh, Azeem |
| Organizer of meeting | Wolak, Frank |
Subjects
| Subject | dynamic discrete choice |
|---|---|
| Subject | counterfactual |
| Subject | partial identification |
| Subject | structural model |
| Genre | Text |
| Genre | Working paper |
| Genre | Grey literature |
Bibliographic information
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- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Kalouptsidi, M., Kitamura, Y., Lima, L., and Souza-Rodrigues, E. (2021). Counterfactual Analysis for Structural Dynamic Discrete Choice Models. Stanford Digital Repository. Available at https://purl.stanford.edu/vn894qd4398
Collection
SITE Conference 2021
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