Estimation and Inference by Stochastic Optimization: Three Examples

• Tác giả: Jean-Jacques Forneron, Serena Ng
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.76 Stochastic systems
• Thông tin xuất bản: 2021
• Bộ sưu tập: Metadata
• ID: 166321

Tóm tắt

Comment: 5 pages, no appendixThis 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.