Indirect inference with a non-smooth criterion function
| Autor: | Dan Zhu, David T. Frazier, Tatsushi Oka |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Economics and Econometrics Mathematical optimization Change of variables General Economics (econ.GN) Computer science Monte Carlo method Derivative Indirect Inference Classification of discontinuities Statistics - Computation 01 natural sciences Methodology (stat.ME) FOS: Economics and business 010104 statistics & probability 0502 economics and business 0101 mathematics Statistics - Methodology Computation (stat.CO) Economics - General Economics 050205 econometrics Applied Mathematics 05 social sciences Bandwidth (signal processing) Criterion function Kernel smoother |
| Zdroj: | Journal of Econometrics. 212:623-645 |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2019.06.003 |
| Popis: | 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. Comment: This paper is a revision of arXiv:1708.02365 and supersedes the earlier arXiv paper "Derivative-Based Optimization with a Non-Smooth Simulated Criterion" |
| Databáze: | OpenAIRE |
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