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

27 Pages Posted: 7 Jun 2024 Last revised: 29 Apr 2025

See all articles by Yanhao 'Max' Wei

Yanhao 'Max' Wei

University of Southern California - Marshall School of Business

Zhenling Jiang

University of Pennsylvania - The Wharton School

Date Written: April 28, 2025

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 the construction of the estimator. 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 the pretrained estimator on 14 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.

Suggested Citation

Wei, Yanhao and Jiang, Zhenling, Pre-Training Estimators for Structural Models: Application to Consumer Search (April 28, 2025). The Wharton School Research Paper, Available at SSRN: https://ssrn.com/abstract=4856490 or http://dx.doi.org/10.2139/ssrn.4856490

Yanhao Wei (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

Zhenling Jiang

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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