Estimation and Inference by Stochastic Optimization: Three Examples

Jean-Jacques Forneron and Serena Ng ()

Papers from arXiv.org

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.

Date: 2021-02
New Economics Papers: this item is included in nep-ecm
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Journal Article: Estimation and Inference by Stochastic Optimization: Three Examples (2021)
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