Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.

Economic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.

A concise and rigorous introduction to widely used approaches in structural econometric modeling Structural econometric modeling specifies the structure of an economic model and estimates the model’s parameters from real-world data. Structural econometric modeling enables better economic theory–based predictions and policy counterfactuals. This book offers a primer on recent developments in these modeling techniques, which are used widely in empirical industrial organization, quantitative marketing, and related fields. It covers such topics as discrete choice modeling, demand modes, estimation of the firm entry models with strategic interactions, consumer search, and theory/empirics of auctions. The book makes highly technical material accessible to graduate students, describing key insights succinctly but without sacrificing rigor. • Concise overview of the most widely used structural econometric models • Rigorous and systematic treatment of the topics, emphasizing key insights • Coverage of demand estimation, estimation of static and dynamic game theoretic models, consumer search, and auctions • Focus on econometric models while providing concise reviews of relevant theoretical models

The Theory of Industrial Organization is the first primary text to treat the new industrial organization at the advanced-undergraduate and graduate level. Rigorously analytical and filled with exercises coded to indicate level of difficulty, it provides a unified and modern treatment of the field with accessible models that are simplified to highlight robust economic ideas while working at an intuitive level. To aid students at different levels, each chapter is divided into a main text and supplementary section containing more advanced material. Each chapter opens with elementary models and builds on this base to incorporate current research in a coherent synthesis. Tirole begins with a background discussion of the theory of the firm. In Part I he develops the modern theory of monopoly, addressing single product and multi product pricing, static and intertemporal price discrimination, quality choice, reputation, and vertical restraints. In Part II, Tirole takes up strategic interaction between firms, starting with a novel treatment of the Bertrand-Cournot interdependent pricing problem. He studies how capacity constraints, repeated interaction, product positioning, advertising, and asymmetric information affect competition or tacit collusion. He then develops topics having to do with long term competition, including barriers to entry, contestability, exit, and research and development. He concludes with a "game theory user's manual" and a section of review exercises. Important Notice: The digital edition of this book is missing some of the images found in the physical edition.

This book covers the main topics that students need to learn in a course on Industrial Organization. It reviews the classic models and important empirical evidence related to the field. However, it will differ from prior textbooks in two ways. First, this book incorporates contributions from behavioral economics and neuroeconomics, providing the reader with a richer understanding of consumer preferences and the motivation for many of the business practices we see today. The book discusses how firms exploit consumers who are prone to making mistakes and who suffer from cognitive dissonance, attention lapses, and bounded rationality, for example and will help explain why firms invest in persuasive advertising, offer 30-day free trials, offer money-back guarantees, and engage in other observed phenomena that cannot be explained by the traditional approaches to industrial organization. A second difference is that this book achieves a balance between textbooks that emphasize formal modeling and those that emphasize the history of the field, empirical evidence, case studies, and policy analysis. This text puts more emphasis on the micro-foundations (i.e., consumer and producer theory), classic game theoretic models, and recent contributions from behavioral economics that are pertinent to industrial organization. Each topic will begin with a discussion of relevant theory and models and will also include a discussion of concrete examples, empirical evidence, and evidence from case studies. This will provide students with a deeper understanding of firm and consumer behavior, of the factors that influence market structure and economic performance, and of policy issues involving imperfectly competitive markets. The book is intended to be a textbook for graduate students, MBAs and upper-level undergraduates and will use examples, graphical analysis, algebra, and simple calculus to explain important ideas and theories in industrial organization.

An issue-driven introduction to industrial organization, thoroughly updated and revised. The study of industrial organization (IO)—the analysis of the way firms compete with one another—has become a key component of economics and of such related disciplines as finance, strategy, and marketing. This book provides an issue-driven introduction to industrial organization. Although formal in its approach, it is written in a way that requires only basic mathematical training. It includes a vast array of examples, from both within and outside the United States. This second edition has been thoroughly updated and revised. In addition to updated examples, this edition presents a more systematic treatment of public policy implications. It features added advanced sections, with analytical treatment of ideas previously presented verbally; and exercises, which allow for a deeper and more formal understanding of each topic. The new edition also includes an introduction to such empirical methods as demand estimation and equilibrium identification. Supplemental material is available online.

The Handbook of Behavioral Industrial Organization integrates behavioral economics into industrial organization. Chapters cover concepts such as relative thinking, salience, shrouded attributes, cognitive dissonance, motivated reasoning, confirmation bias, overconfidence, status quo bias, social cooperation and identity. Additional chapters consider industry issues, such as sports and gambling industries, neuroeconomic studies of brands and advertising, and behavioral antitrust law. The Handbook features a wide array of methods (literature surveys, experimental and econometric research, and theoretical modelling), facilitating accessibility to a wide audience.

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.

This volume focuses on recent developments in the use of structural econometric models in empirical economics. The first part looks at recent developments in the estimation of dynamic discrete choice models. The second part looks at recent advances in the area empirical matching models.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.