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.

翻译：本文介绍了Forneron & Ng (202020年)中设计的两个算法:重新标注的牛顿-拉夫森(rNR)和重新标注的准牛顿(rqN)算法,这些算法加速估算和结构模型的靴套推断。对BLP的实验应用显示,计算时间从标准靴陷阱的近5小时减少到与RNR的仅仅1小时以上,而使用rqN的只有15分钟。 第一次蒙特-卡洛演练显示了Probit IV回归中估算和推断方法的准确性。 第二项演练还用动态面板回归示例说明了与模拟估算标准估算相比的统计效率收益。