Indirect Inference With a Non-Smooth Criterion Function

50 Pages Posted: 25 Aug 2018 Last revised: 9 Jul 2019

See all articles by David Frazier

David Frazier

Monash Business School

Tatsushi Oka

Keio University

Dan Zhu

Monash University - Department of Econometrics & Business Statistics

Date Written: August 5, 2018

Abstract

Indirect inference requires simulating realisations 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 optimisation 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 optimisation 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

JEL Classification: C10, C13, C15, C25

Suggested Citation

Frazier, David and Oka, Tatsushi and Zhu, Dan, Indirect Inference With a Non-Smooth Criterion Function (August 5, 2018). Available at SSRN: https://ssrn.com/abstract=3226391 or http://dx.doi.org/10.2139/ssrn.3226391

David Frazier (Contact Author)

Monash Business School ( email )

Wellington Road
Clayton, Victoria 3168
Australia

Tatsushi Oka

Keio University ( email )

Japan

Dan Zhu

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

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