Indirect inference with a non-smooth criterion function

David T. Frazier, Tatsushi Oka, Dan Zhu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)623-645
Number of pages23
JournalJournal of Econometrics
Volume212
Issue number2
DOIs
Publication statusPublished - 2019 Oct
Externally publishedYes

Keywords

  • Discontinuous objective functions
  • Dynamic discrete choice models
  • Indirect inference
  • Simulation estimators

ASJC Scopus subject areas

  • Economics and Econometrics

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