A Simulated Maximum Likelihood (SML) estimator for the random coefficient logit model using aggregate data is found to be more efficient than the widely used Generalized Method of Moments estimator (GMM) of Berry-Levinsohn-Pakes (1995). In particular, the SML estimator is better than the GMM...

We propose a Simulated Maximum Likelihood estimation method for the random coefficient logit model using aggregate data, accounting for heterogeneity and endogeneity. Our method allows for two sources of randomness in observed market shares - unobserved product characteristics and sampling...

A Simulated Maximum Likelihood (SML) estimator for the random coefficient logit model using aggregate data is found to be more efficient than the widely used Generalized Method of Moments estimator (GMM) of Berry-Levinsohn-Pakes (1995). In particular, the SML estimator is better than the GMM...