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 et al. (Econometrica 63:841–890, <CitationRef CitationID="CR4">1995</CitationRef>). In particular, the SML estimator is...</citationref>

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...

Different objectives such as category demand expansion or market share stealing warrant the use of different marketing instruments. To help brand managers make informed decisions, it is essential that marketing mix models appropriately measure their effects. Random Utility Models (RUM) that have...

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...