  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.
