Partial Identi?cation and Inference for Dynamic Models and Counterfactuals

Myrto Kalouptsidi, Yuichi Kitamura (yuichi.kitamura@yale.edu), Lucas Lima and Eduardo Souza-Rodrigues
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Myrto Kalouptsidi: Institute for Fiscal Studies
Yuichi Kitamura: Institute for Fiscal Studies and Yale University
Lucas Lima: Institute for Fiscal Studies
Eduardo Souza-Rodrigues: Institute for Fiscal Studies

No CWP6/20, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We provide a general framework for investigating partial identi?cation 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 e?ects 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 identi?ed 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 ?rm 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 identi?ed sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identi?cation.

Date: 2020-02-10
New Economics Papers: this item is included in nep-dcm and nep-ore
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Citations: View citations in EconPapers (4)

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