Princeton University Press
Learning and Expectations in Macroeconomics
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About this book
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.
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Frontmatter
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Contents
ix -
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Preface
xv - Part I. View of the Landscape
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Chapter 1. Expectations and the Learning Approach
5 -
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Chapter 2. Introduction to the Techniques
25 -
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Chapter 3. Variations on a Theme
45 -
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Chapter 4. Applications
59 - Part II. Mathematical Background and Tools
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Chapter 5. The Mathematical Background
87 -
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Chapter 6. Tools: Stochastic Approximation
121 -
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Chapter 7. Further Topics in Stochastic Approximation
147 - Part III. Learning in Linear Models
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Chapter 8. Univariate Linear Models
173 -
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Chapter 9. Further Topics in Linear Models
205 -
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Chapter 10. Multivariate Linear Models
227 - Part IV. Learning in Nonlinear Models
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Chapter 11. Nonlinear Models: Steady States
267 -
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Chapter 12. Cycles and Sunspot Equilibria
287 - Part V. Further Topics
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Chapter 13. Misspecification and Learning
317 -
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Chapter 14. Persistent Learning Dynamics
331 -
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Chapter 15. Extensions and Other Approaches
361 -
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Chapter 16. Conclusions
385 -
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Bibliography
389 -
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Author Index
407 -
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Subject Index
411