Estimation of random coefficients logit demand models with interactive fixed effects

TL;DR

This paper extends the BLP demand model by incorporating interactive fixed effects to better handle endogeneity and persistent market share patterns, proposing a computationally efficient LS-MD estimator.

Contribution

It introduces a novel extension with interactive fixed effects in demand models, along with a practical two-step LS-MD estimation method.

Findings

Estimator performs well in Monte Carlo simulations.

The model captures strong persistence in market shares.

Empirical application to US automobile demand demonstrates usefulness.

Abstract

We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discrete-choice demand model, which underlies much recent empirical work in IO. We add interactive fixed effects in the form of a factor structure on the unobserved product characteristics. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics (including price), which accommodates endogeneity and, at the same time, captures strong persistence in market shares across products and markets. We propose a two-step least squares-minimum distance (LS-MD) procedure to calculate the estimator. Our estimator is easy to compute, and Monte Carlo simulations show that it performs well. We consider an empirical illustration to US automobile demand.