Estimation and Inference by Stochastic Optimization: Three Examples
Econometrics
2021-02-23 v1 Methodology
Abstract
This paper illustrates two algorithms designed in Forneron & Ng (2020): the resampled Newton-Raphson (rNR) and resampled quasi-Newton (rqN) algorithms which speed-up estimation and bootstrap inference for structural models. An empirical application to BLP shows that computation time decreases from nearly 5 hours with the standard bootstrap to just over 1 hour with rNR, and only 15 minutes using rqN. A first Monte-Carlo exercise illustrates the accuracy of the method for estimation and inference in a probit IV regression. A second exercise additionally illustrates statistical efficiency gains relative to standard estimation for simulation-based estimation using a dynamic panel regression example.
Cite
@article{arxiv.2102.10443,
title = {Estimation and Inference by Stochastic Optimization: Three Examples},
author = {Jean-Jacques Forneron and Serena Ng},
journal= {arXiv preprint arXiv:2102.10443},
year = {2021}
}
Comments
5 pages, no appendix