Standard discrete choice models such as logit, nested logit, and random coefficients models place very strong restrictions on how unobservable product space increases with the number of products. We argue (and show with Monte Carlo experiments) that these restrictions can lead to biased conclusions regarding price elasticities and welfare consequences from additional products. In addition, these restrictions can identify parameters which are not intuitively identified given the data at hand. We suggest two alternative models that relax these restrictions, both motivated by structural interpretations. Monte-Carlo experiments and an application to data show that these alternative models perform well in practice

"The discrete choice approach provides an ideal framework for describing the demands for differentiated products and can be used for studying most product differentiation models in the literature. By introducing extra dimensions of product heterogeneity, the framework also provides richer models of firm location and product selection."--BOOK JACKET.

We extend BLP's aggregate discrete-choice model of product differentiation to create more flexibility in the price functional form. We apply a Box-Cox specification, which relaxes the typical unit demand assumption and creates flexibility on demand curvature. The model provides a unifying framework for mixed logit and mixed CES models. Our illustrative application to the ready-to-eat cereals market shows that the cross-sectional relation between price elasticities and average prices per product is more in line with descriptive elasticity patterns. Furthermore, it suggests lower cross-price elasticities between similarly priced products than in more restrictive specifications.

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

The study of differentiated-products markets is a central part of empirical industrial organization. Questions regarding market power, mergers, innovation, and valuation of new brands are addressed using cutting-edge econometric methods and relying on economic theory. Unfortunately, difficulty of use and computational costs have limited the scope of application of recent developments in one of the main methods for estimating demand for differentiated products: random coefficients discrete choice models. As our understanding of these models of demand has increased, both the difficulty and costs have been greatly reduced. This paper carefully discusses the latest innovations in these methods with the hope of (1) increasing the understanding, and therefore the trust, among researchers who never used these methods, and (2) reducing the difficulty of use, and therefore aiding in realizing the full potential of these methods.