Estimating Parameters of Structural Models Using Neural Networks

TL;DR

This paper introduces a neural network-based estimator for structural econometric models that provides accurate parameter estimates and statistical accuracy, reducing computational costs compared to traditional methods.

Contribution

The paper presents a novel neural network estimator for structural models that improves estimation accuracy and computational efficiency, especially in complex models.

Findings

Neural network estimator closely approximates Bayesian posterior.

NNE achieves higher accuracy with lower computational costs.

Robust to redundant moment inputs.

Abstract

We study an alternative use of machine learning. We train neural nets to provide the parameter estimate of a given (structural) econometric model, for example, discrete choice or consumer search. Training examples consist of datasets generated by the econometric model under a range of parameter values. The neural net takes the moments of a dataset as input and tries to recognize the parameter value underlying that dataset. Besides the point estimate, the neural net can also output statistical accuracy. This neural net estimator (NNE) tends to limited-information Bayesian posterior as the number of training datasets increases. We apply NNE to a consumer search model. It gives more accurate estimates at lighter computational costs than the prevailing approach. NNE is also robust to redundant moment inputs. In general, NNE offers the most benefits in applications where other estimation approaches require very heavy simulation costs. We provide code at: https://nnehome.github.io.