Gorman Lectures in Economics – serie

A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these tools to create a class of econometrically tractable models of prices and quantities. They present examples from microeconomics, macroeconomics, and asset pricing. The models are cast in terms of a representative consumer. While Hansen and Sargent demonstrate the analytical benefits acquired when an analysis with a representative consumer is possible, they also characterize the restrictiveness of assumptions under which a representative household justifies a purely aggregative analysis. Hansen and Sargent unite economic theory with a workable econometrics while going beyond and beneath demand and supply curves for dynamic economies. They construct and apply competitive equilibria for a class of linear-quadratic-Gaussian dynamic economies with complete markets. Their book, based on the 2012 Gorman lectures, stresses heterogeneity, aggregation, and how a common structure unites what superficially appear to be diverse applications.An appendix describes MATLAB programs that apply to the book's calculations.

How can property rights be protected and contracts be enforced in countries where the rule of law is ineffective or absent? How can firms from advanced market economies do business in such circumstances? In Lawlessness and Economics, Avinash Dixit examines the theory of private institutions that transcend or supplement weak economic governance from the state. In much of the world and through much of history, private mechanisms--such as long-term relationships, arbitration, social networks to disseminate information and norms to impose sanctions, and for-profit enforcement services--have grown up in place of formal, state-governed institutions. Even in countries with strong legal systems, many of these mechanisms continue under the shadow of the law. Numerous case studies and empirical investigations have demonstrated the variety, importance, and merits, and drawbacks of such institutions. This book builds on these studies and constructs a toolkit of theoretical models to analyze them.The models shed new conceptual light on the different modes of governance, and deepen our understanding of the interaction of the alternative institutions with each other and with the government's law. For example, one model explains the limit on the size of social networks and illuminates problems in the transition to more formal legal systems as economies grow beyond this limit. Other models explain why for-profit enforcement is inefficient. The models also help us understand why state law dovetails with some non-state institutions and collides with others. This can help less-developed countries and transition economies devise better processes for the introduction or reform of their formal legal systems.

Individuals and families make key decisions that impact many aspects of financial stability and determine the future of the economy. These decisions involve balancing current sacrifice against future benefits. People have to decide how much to invest in health care, exercise, their diet, and insurance. They must decide how much debt to take on, and how much to save. And they make choices about jobs that determine employment and unemployment levels. Forward-Looking Decision Making is about modeling this individual or family-based decision making using an optimizing dynamic programming model. Robert Hall first reviews ideas about dynamic programs and introduces new ideas about numerical solutions and the representation of solved models as Markov processes. He surveys recent research on the parameters of preferences--the intertemporal elasticity of substitution, the Frisch elasticity of labor supply, and the Frisch cross-elasticity. He then examines dynamic programming models applied to health spending, long-term care insurance, employment, entrepreneurial risk-taking, and consumer debt.Linking theory with data and applying them to real-world problems, Forward-Looking Decision Making uses dynamic optimization programming models to shed light on individual behaviors and their economic implications.

It is widely held that Bayesian decision theory is the final word on how a rational person should make decisions. However, Leonard Savage--the inventor of Bayesian decision theory--argued that it would be ridiculous to use his theory outside the kind of small world in which it is always possible to "look before you leap." If taken seriously, this view makes Bayesian decision theory inappropriate for the large worlds of scientific discovery and macroeconomic enterprise. When is it correct to use Bayesian decision theory--and when does it need to be modified? Using a minimum of mathematics, Rational Decisions clearly explains the foundations of Bayesian decision theory and shows why Savage restricted the theory's application to small worlds. The book is a wide-ranging exploration of standard theories of choice and belief under risk and uncertainty. Ken Binmore discusses the various philosophical attitudes related to the nature of probability and offers resolutions to paradoxes believed to hinder further progress.In arguing that the Bayesian approach to knowledge is inadequate in a large world, Binmore proposes an extension to Bayesian decision theory--allowing the idea of a mixed strategy in game theory to be expanded to a larger set of what Binmore refers to as "muddled" strategies. Written by one of the world's leading game theorists, Rational Decisions is the touchstone for anyone needing a concise, accessible, and expert view on Bayesian decision making.

How corporations have increasingly embraced the sharing of knowledge as a competitive strategyInnovation is more important than ever, yet breakthroughs are becoming harder to achieve. Modern technologies, from artificial intelligence and 5G networks to biotech, are so complex that they require collaboration across companies, industries, and countries. Increasingly, firms are discovering that sharing knowledge can be more powerful, and more profitable, than guarding it. In Rethinking Innovation, distinguished economists Josh Lerner and Jean Tirole examine ways that corporations have embraced the sharing of knowledge as a competitive strategy. Open-source software, for example, has seen multibillion-dollar public offerings and shifting business strategies, producing high-profile disputes over what openness actually entails. Across high-tech industries, new forms of knowledge-sharing are transforming how companies develop and exploit new technologies.Lerner and Tirole explore three key organizational structures that make this collaboration possible: patent pools, which bundle essential patents and make them available to firms seeking to license them; standard-setting organizations, where companies agree on common technical templates and commit to making related patents available on fair terms; and open-source communities, in which users share innovations freely with others. Drawing on decades of pioneering research, Lerner and Tirole explain how these institutions emerged, how they work in practice, and how they can both promote innovation and create new challenges for competition and policy. Combining economic insight with vivid case studies—from early efforts to coordinate aircraft patents to contemporary battles over artificial intelligence and global technology standards—Rethinking Innovation reveals how firms collaborate to develop and commercialize complex technologies. Clear and accessible throughout, this book offers a guide to the forces reshaping modern innovation for managers, policymakers, and scholars.

Over the past few decades, matching models, which use mathematical frameworks to analyze allocation mechanisms for heterogeneous products and individuals, have attracted renewed attention in both theoretical and applied economics. These models have been used in many contexts, from labor markets to organ donations, but recent work has tended to focus on "nontransferable" cases rather than matching models with transfers. In this important book, Pierre-Andre Chiappori fills a gap in the literature by presenting a clear and elegant overview of matching with transfers and provides a set of tools that enable the analysis of matching patterns in equilibrium, as well as a series of extensions. He then applies these tools to the field of family economics and shows how analysis of matching patterns and of the incentives thus generated can contribute to our understanding of long-term economic trends, including inequality and the demand for higher education.

A guide to the economic modeling of household preferences, from two leaders in the fieldA common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these tools to create a class of econometrically tractable models of prices and quantities. They present examples from microeconomics, macroeconomics, and asset pricing. The models are cast in terms of a representative consumer. While Hansen and Sargent demonstrate the analytical benefits acquired when an analysis with a representative consumer is possible, they also characterize the restrictiveness of assumptions under which a representative household justifies a purely aggregative analysis.Hansen and Sargent unite economic theory with a workable econometrics while going beyond and beneath demand and supply curves for dynamic economies. They construct and apply competitive equilibria for a class of linear-quadratic-Gaussian dynamic economies with complete markets. Their book, based on the 2012 Gorman lectures, stresses heterogeneity, aggregation, and how a common structure unites what superficially appear to be diverse applications. An appendix describes MATLAB programs that apply to the book's calculations.