Arbeitspapier
Partial identification and inference for dynamic models and counterfactuals
We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We characterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference procedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP6/20
- Classification
-
Wirtschaft
- Subject
-
Dynamic Discrete Choice
Counterfactual
Partial Identification
Subsampling
Uniform Inference
Structural Model
- Event
-
Geistige Schöpfung
- (who)
-
Kalouptsidi, Myrto
Kitamura, Yuichi
Lucas Lima
Rodrigues, Eduardo Augusto de Souza
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
-
London
- (when)
-
2020
- DOI
-
doi:10.1920/wp.cem.2020.620
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kalouptsidi, Myrto
- Kitamura, Yuichi
- Lucas Lima
- Rodrigues, Eduardo Augusto de Souza
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2020
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