"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.

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

There are few industries in modern market economies that do not manufacture differentiated products. This book provides a systematic explanation and analysis of the widespread prevalence of this important category of products. The authors concentrate on models in which product selection is endogenous. In the first four chapters they consider models that try to predict the level of product differentiation that would emerge in situations of market equilibrium. These market equilibria with differentiated products are characterised and then compared with social welfare optima. Particular attention is paid to the distinction between horizontal and vertical differentiation as well as to the related issues of product quality and durability. This book brings together the most important theoretical contributions to these topics in a succinct and coherent manner. One of its major strengths is the way in which it carefully sets out the basic intuition behind the formal results. It will be useful to advanced undergraduate and graduate students taking courses in industrial economics and microeconomic theory.

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.