Learning and Expectations in Macroeconomics / / George W. Evans, Seppo Honkapohja.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Evans, George W.,; Honkapohja, Seppo,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2012]; ©2001
• Year of Publication: 2012
• Edition: Core Textbook
• Language: English
• Series: Frontiers of Economic Research
• Physical Description: 1 online resource (424 p.)

A crucial challenge for economists is figuring out how people interpret the world and form expectations that will likely influence their economic activity. Inflation, asset prices, exchange rates, investment, and consumption are just some of the economic variables that are largely explained by expectations. Here George Evans and Seppo Honkapohja bring new explanatory power to a variety of expectation formation models by focusing on the learning factor. Whereas the rational expectations paradigm offers the prevailing method to determining expectations, it assumes very theoretical knowledge on the part of economic actors. Evans and Honkapohja contribute to a growing body of research positing that households and firms learn by making forecasts using observed data, updating their forecast rules over time in response to errors. This book is the first systematic development of the new statistical learning approach. Depending on the particular economic structure, the economy may converge to a standard rational-expectations or a "rational bubble" solution, or exhibit persistent learning dynamics. The learning approach also provides tools to assess the importance of new models with expectational indeterminacy, in which expectations are an independent cause of macroeconomic fluctuations. Moreover, learning dynamics provide a theory for the evolution of expectations and selection between alternative equilibria, with implications for business cycles, asset price volatility, and policy. This book provides an authoritative treatment of this emerging field, developing the analytical techniques in detail and using them to synthesize and extend existing research.

Table of Contents

Frontmatter -- Contents -- Preface -- Part I. View of the Landscape -- Chapter 1. Expectations and the Learning Approach -- Chapter 2. Introduction to the Techniques -- Chapter 3. Variations on a Theme -- Chapter 4. Applications -- Part II. Mathematical Background and Tools -- Chapter 5. The Mathematical Background -- Chapter 6. Tools: Stochastic Approximation -- Chapter 7. Further Topics in Stochastic Approximation -- Part III. Learning in Linear Models -- Chapter 8. Univariate Linear Models -- Chapter 9. Further Topics in Linear Models -- Chapter 10. Multivariate Linear Models -- Part IV. Learning in Nonlinear Models -- Chapter 11. Nonlinear Models: Steady States -- Chapter 12. Cycles and Sunspot Equilibria -- Part V. Further Topics -- Chapter 13. Misspecification and Learning -- Chapter 14. Persistent Learning Dynamics -- Chapter 15. Extensions and Other Approaches -- Chapter 16. Conclusions -- Bibliography -- Author Index -- Subject Index