Working Paper: NBER ID: w26761
Authors: Myrto Kalouptsidi; Yuichi Kitamura; Lucas Lima; Eduardo A. Souza-Rodrigues
Abstract: 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 char- acterize 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 pro- cedure 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.
Keywords: dynamic discrete choice models; partial identification; counterfactual analysis; inference procedures
JEL Codes: C0; C1; F0; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| model assumptions (C51) | precision of counterfactual predictions (C53) |
| alternative model restrictions incorporated (C24) | identified sets for counterfactual behaviors shrink (D91) |
| model compatibility with data (C52) | identified set can be empty/singleton/continuum (C20) |
| inference procedure is uniformly valid (C20) | variety of counterfactual experiments (C93) |
| Monte Carlo study demonstrates effectiveness of method (C52) | finite samples (C34) |
| identified sets lead to valid inference procedures (C20) | understanding impact of policy interventions (D78) |