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

Abstract

We explore pretraining estimators for structural econometric models. The estimator is “pretrained” in the sense that the bulk of the computational cost and researcher effort occur during its construction. Subsequent applications of the estimator to different datasets require little computational cost or researcher effort. The estimation leverages a neural net to recognize the structural model's parameter from data patterns. As an initial trial, this paper builds a pretrained estimator for a sequential search model that is known to be difficult to estimate. We evaluate it on 12 real datasets. The estimation takes seconds to run and shows high accuracy. We provide the estimator at pnnehome.github.io. More generally, pretrained, off-the-shelf estimators can make structural models more accessible to researchers and practitioners.