Estimation and Inference by Stochastic Optimization: Three Examples

2021-02-20 · Jean-Jacques Forneron, Serena Ng

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.

📄 PDF Abstract BibTeX arXiv:2102.10443

Code (0)

등록된 구현이 없습니다.

Tasks

regression Stochastic Optimization

Stochastic Variational Inference with Gradient Linearization

2018-03-28 · CVPR 2018 6 · Tobias Plötz, Anne S. Wannenwetsch, Stefan Roth

Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benefit is that stochastic variational infere…

Denoising Optical Flow Estimation Surface Reconstruction Variational Inference

Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians

2024-11-21 · William N. Caballero, Matthew LaRosa, Alexander Fisher, Vahid Tarokh

The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagrams, and variational autoencoders). Howeve…

Bayesian Optimization

Recursive Algorithms for Sparse Parameter Identification of Multivariate Stochastic Systems with Non-stationary Observations

2025-05-01 · Yanxin Fu, Wenxiao Zhao

The classical sparse parameter identification methods are usually based on the iterative basis selection such as greedy algorithms, or the numerical optimization of regularized cost functions such as LASSO and Bayesian p…