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Title: Partial Identification and Inference for Dynamic Models and Counterfactuals

Author(s): Myrto Kalouptsidi, Yuichi Kitamura, Lucas Lima and Eduardo Souza-Rodrigues

Publication Date: February 2020

Keyword(s): counterfactual, dynamic discrete choice, Partial identification, Structural Model, Subsampling and Uni- form Inference

Programme Area(s): Industrial Organization, International Trade and Regional Economics and Labour Economics

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.

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Bibliographic Reference

Kalouptsidi, M, Kitamura, Y, Lima, L and Souza-Rodrigues, E. 2020. 'Partial Identification and Inference for Dynamic Models and Counterfactuals'. London, Centre for Economic Policy Research.