Pre-Training Estimators for Structural Models: Application to Consumer Search

TL;DR

This paper introduces pre-trained neural network estimators for structural econometric models, enabling rapid, accurate, and reusable parameter estimation across datasets, significantly simplifying the application of complex models.

Contribution

It presents a novel approach to pre-train estimators for structural models, achieving fast and accurate estimation that converges to Bayesian posteriors, and demonstrates its effectiveness on consumer search models.

Findings

Estimation takes only seconds on real datasets.

Achieves high accuracy with negligible computational cost.

Pre-trained estimators make structural modeling more accessible.

Abstract

We develop pre-trained estimators for structural econometric models. The estimator uses a neural net to recognize the structural model's parameter from data patterns. Once trained, the estimator can be shared and applied to different datasets at negligible cost and effort. Under sufficient training, the estimator converges to the Bayesian posterior given the data patterns. As an illustration, we construct a pretrained estimator for a sequential search model (available at pnnehome.github.io). Estimation takes only seconds and achieves high accuracy on 12 real datasets. More broadly, pretrained estimators can make structural models much easier to use and more accessible.