Estimation and Inference by Stochastic Optimization: Three Examples

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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.