Simulated Maximum Likelihood Estimator for the Random Coefficient Logit Model Using Aggregate Data

The authors propose a simulated maximum likelihood estimation method for the random coefficient logit model using aggregate data, accounting for heterogeneity and endogeneity. The method allows for two sources of randomness in observed market shares: unobserved product characteristics and sampling error. Because of the latter, the method is suitable when sample sizes underlying the shares are finite. In contrast, Berry, Levinsohn and Pakes's commonly used approach assumes that observed shares have no sampling error. The method can be viewed as a generalization of Villas-Boas and Winer's approach and is closely related to Petrin and Train's “control function” approach. The authors show that the proposed method provides unbiased and efficient estimates of demand parameters. They also obtain endogeneity test statistics as a by-product, including the direction of endogeneity bias. The model can be extended to incorporate Markov regime-switching dynamics in parameters and is open to other extensions based on maximum likelihood. The benefits of the proposed approach are achieved by assuming normality of the unobserved demand attributes, an assumption that imposes constraints on the types of pricing behaviors that are accommodated. However, the authors find in simulations that demand estimates are fairly robust to violations of these assumptions.