Partial Identification and Inference for Dynamic Models and Counterfactuals

Myrto Kalouptsidi, Yuichi Kitamura, Lucas Lima and Eduardo Souza-Rodrigues

Working Papers from University of Toronto, Department of 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 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.

Keywords: Dynamic Discrete Choice; Counterfactual; Partial Identification; Subsampling; Uniform Inference; Structural Model (search for similar items in EconPapers)
JEL-codes: C0 C18 C50 C61 L0 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2020-02-06
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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