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Learning and Expectations in Macroeconomics

ISBN-10: 0691049211
ISBN-13: 9780691049212
Edition: 2001
List price: $85.00 Buy it from $23.05
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Description: 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  More...

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Book details

List price: $85.00
Copyright year: 2001
Publisher: Princeton University Press
Publication date: 1/28/2001
Binding: Hardcover
Pages: 424
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.694
Language: English

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.

Preface
View of the Landscape
Expectations and the Learning Approach
Expectations in Macroeconomics
Two Examples
Classical Models of Expectation Formation
Learning: The New View of Expectations
Statistical Approach to Learning
A General Framework
Overview of the Book 19
Introduction to the Techniques
Introduction
The Cobweb Model
Econometric Learning
Expectational Stability
Rational vs. Reasonable Learning
Recursive Least Squares
Convergence of Stochastic Recursive Algorithms
Application to the Cobweb Model
The E-Stability Principle
Discussion of the Literature 43
Variations on a Theme
Introduction
Heterogeneous Expectations
Learning with Constant Gain
Learning in Nonstochastic Models
Stochastic Gradient Learning
Learning with Misspecification 56
Applications
Introduction
The Overlapping Generations Model
A Linear Stochastic Macroeconomic Model
The Ramsey Model
The Diamond Growth Model
A Model with Increasing Social Returns
Other Models
Appendix 82
Mathematical Background and Tools
The Mathematical Background
Introduction
Difference Equations
Differential Equations
Linear Stochastic Processes
Markov Processes
Ito Processes
Appendix on Matrix Algebra
References for Mathematical Background 118
Tools: Stochastic Approximation
Introduction
Stochastic Recursive Algorithms
Convergence: The Basic Results
Convergence: Further Discussion
Instability Results
Expectational Stability
Global Convergence 144
Further Topics in Stochastic Approximation
Introduction
Algorithms for Nonstochastic Frameworks
The Case of Markovian State Dynamics
Convergence Results for Constant-Gain Algorithms
Gaussian Approximation for Cases of Decreasing Gain
Global Convergence on Compact Domains
Guide to the Technical Literature 169
Learning in Linear Models
Univariate Linear Models
Introduction
A Special Case
E-Stability and Least Squares Learning: MSV Solutions
E-Stability and Learning: The Full Class of Solutions
Extension 1: Lagged Endogenous Variables
Extension 2: Models with Time-t Dating
Conclusions 204
Further Topics in Linear Models
Introduction
Muth's Inventory Model
Overparameterization in the Special Case
Extended Special Case
Linear Model with Two Forward Leads
Learning Explosive Solutions
Bubbles in Asset Prices
Heterogeneous Learning Rules 223
Multivariate Linear Models
Introduction
MSV Solutions and Learning
Models with Contemporaneous Expectations
Real Business Cycle Model
Irregular REE
Conclusions
Appendix 1: Linearizations
Appendix 2: Solution Techniques 252
Learning in Nonlinear Models Nonlinear Models: Steady States
Introduction
Equilibria under Perfect Foresight
Noisy Steady States
Adaptive Learning for Steady States
E-Stability and Learning
Applications 276
Cycles and Sunspot Equilibria
Introduction
Overview of Results
Deterministic Cycles
Noisy Cycles
Existence of Sunspot Equilibria
Learning SSEs
Global Analysis of Learning Dynamics
Conclusions 313
Further Topics 1

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