Indirect inference with a non-smooth criterion function

David T. Frazier, Tatsushi Oka and Dan Zhu

Journal of Econometrics, 2019, vol. 212, issue 2, 623-645

Abstract: Indirect inference requires simulating realizations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of derivative-based optimization routines. Using a change of variables technique, we propose a novel simulation algorithm that alleviates the discontinuities inherent in such indirect inference criterion functions, and permits the application of derivative-based optimization routines to estimate the unknown model parameters. Unlike competing approaches, this approach does not rely on kernel smoothing or bandwidth parameters. Several Monte Carlo examples that have featured in the literature on indirect inference with discontinuous outcomes illustrate the approach, and demonstrate the superior performance of this approach over existing alternatives.

Keywords: Simulation estimators; Indirect inference; Discontinuous objective functions; Dynamic discrete choice models (search for similar items in EconPapers)
JEL-codes: C10 C13 C15 C25 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Working Paper: Indirect Inference with a Non-Smooth Criterion Function (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:2:p:623-645

DOI: 10.1016/j.jeconom.2019.06.003

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