Estimation and Inference by Stochastic Optimization: Three Examples

Jean-Jacques Forneron; Serena Ng

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arXiv · 2102.10443

Estimation and Inference by Stochastic Optimization: Three Examples

Jean-Jacques Forneron·Serena Ng

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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.

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BibTeX RIS

Jean-Jacques Forneron, Serena Ng. 2021-02-20. Estimation and Inference by Stochastic Optimization: Three Examples. https://arxiv.org/abs/2102.10443

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