Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks

TL;DR

This paper introduces a Bayesian method for estimating discrete choice demand models with sparse market-product shocks, avoiding demand inversion and IVs, and validated through simulations and empirical data.

Contribution

It develops a nonparametric identification and Bayesian inference approach leveraging sparsity, offering an alternative to the standard BLP method without requiring demand inversion or IVs.

Findings

Effective in identifying demand shocks under sparsity.

Outperforms standard methods in simulations.

Empirical evidence supports sparse shocks in datasets.

Abstract

We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under mild conditions. Then we develop a Bayesian procedure, which exploits the sparsity structure using shrinkage priors, to conduct inference about the model parameters and counterfactual quantities. Comparing to the standard BLP (Berry, Levinsohn, & Pakes, 1995) method, our approach does not require demand inversion or instrumental variables (IVs), thus provides a compelling alternative when IVs are not available or their validity is questionable. Monte Carlo simulations validate our theoretical findings and demonstrate the effectiveness of our approach, while empirical applications reveal evidence of sparse demand shocks in well-known datasets.