Estimation of Discrete Choice Models: A Machine Learning Approach

TL;DR

This paper introduces a novel machine learning-based estimation method for discrete choice demand models that enhances accuracy and efficiency by combining nonparametric prediction with moment estimation, especially when individual data is available.

Contribution

The paper presents a new two-step estimation approach called NAME that improves upon standard methods by integrating machine learning predictions with structural parameter estimation.

Findings

Improves estimation precision over traditional methods

Reduces convergence time in simulations

Provides large sample properties of the estimator

Abstract

In this paper we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed individual level characteristics. Step 2 estimates the structural parameters of the model using the estimated choice probabilities at a particular point of interest and the moment restrictions. In essence, the method uses nonparametric approximation (followed by) moment estimation. Hence the name---NAME. We use simulations to compare the performance of NAME with the standard methodology. We find that our method improves precision as well as convergence time. We supplement the analysis by providing the large sample properties of the proposed estimator.