Estimation and Inference by Stochastic Optimization: Three Examples

Jean-Jacques Forneron and Serena Ng ()

AEA Papers and Proceedings, 2021, vol. 111, 626-30

Abstract: This paper illustrates two algorithms designed in Forneron and 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 five hours with the standard bootstrap to just over one hour with rNR and to 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.

JEL-codes: C15 C23 C25 C26 C61 C63 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Working Paper: Estimation and Inference by Stochastic Optimization: Three Examples (2021)
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DOI: 10.1257/pandp.20211038

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