Learning and Expectations in Macroeconomics ：Learning and Expectations in Macroeconomics ( Frontiers of Economic Research )

Publication subTitle ：Learning and Expectations in Macroeconomics

Publication series ：Frontiers of Economic Research

Author： Evans George W.;Honkapohja Seppo;;

Publisher： Princeton University Press‎

Publication year： 2012

E-ISBN: 9781400824267

P-ISBN(Paperback): 9780691049212

Subject： F019.4 reasonable expectation

Keyword：世界各国经济概况、经济史、经济地理

Language： ENG

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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 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 vola

Chapter

Part I: View of the Landscape

1 Expectations and the Learning Approach

1.1 Expectations in Macroeconomics

1.2 Two Examples

1.3 Classical Models of Expectation Formation

1.4 Learning: The New View of Expectations

1.5 Statistical Approach to Learning

1.6 A General Framework

1.7 Overview of the Book

2 Introduction to the Techniques

2.1 Introduction

2.2 The Cobweb Model

2.3 Econometric Learning

2.4 Expectational Stability

2.5 Rational vs. Reasonable Learning

2.6 Recursive Least Squares

2.7 Convergence of Stochastic Recursive Algorithms

2.8 Application to the Cobweb Model

2.9 The E-Stability Principle

2.10 Discussion of the Literature

3 Variations on a Theme

3.1 Introduction

3.2 Heterogeneous Expectations

3.3 Learning with Constant Gain

3.4 Learning in Nonstochastic Models

3.5 Stochastic Gradient Learning

3.6 Learning with Misspecification

4 Applications

4.1 Introduction

4.2 The Overlapping Generations Model

4.3 A Linear Stochastic Macroeconomic Model

4.4 The Ramsey Model

4.5 The Diamond Growth Model

4.6 A Model with Increasing Social Returns

4.7 Other Models

4.8 Appendix

Part II: Mathematical Background and Tools

5 The Mathematical Background

5.1 Introduction

5.2 Difference Equations

5.3 Differential Equations

5.4 Linear Stochastic Processes

5.5 Markov Processes

5.6 Ito Processes

5.7 Appendix on Matrix Algebra

5.8 References for Mathematical Background

6 Tools: Stochastic Approximation

6.1 Introduction

6.2 Stochastic Recursive Algorithms

6.3 Convergence: The Basic Results

6.4 Convergence: Further Discussion

6.5 Instability Results

6.6 Expectational Stability

6.7 Global Convergence

7 Further Topics in Stochastic Approximation

7.1 Introduction

7.2 Algorithms for Nonstochastic Frameworks

7.3 The Case of Markovian State Dynamics

7.4 Convergence Results for Constant-Gain Algorithms

7.5 Gaussian Approximation for Cases of Decreasing Gain

7.6 Global Convergence on Compact Domains

7.7 Guide to the Technical Literature

Part III: Learning in Linear Models

8 Univariate Linear Models

8.1 Introduction

8.2 A Special Case

8.3 E-Stability and Least Squares Learning: MSV Solutions

8.4 E-Stability and Learning: The Full Class of Solutions

8.5 Extension 1: Lagged Endogenous Variables

8.6 Extension 2: Models with Time-t Dating

8.7 Conclusions

9 Further Topics in Linear Models

9.1 Introduction

9.2 Muth’s Inventory Model

9.3 Overparameterization in the Special Case

9.4 Extended Special Case

9.5 Linear Model with Two Forward Leads

9.6 Learning Explosive Solutions

9.7 Bubbles in Asset Prices

9.8 Heterogeneous Learning Rules

10 Multivariate Linear Models

10.1 Introduction

10.2 MSV Solutions and Learning

10.3 Models with Contemporaneous Expectations

10.4 Real Business Cycle Model

10.5 Irregular REE

10.6 Conclusions

10.7 Appendix 1: Linearizations

10.8 Appendix 2: Solution Techniques

Part IV: Learning in Nonlinear Models

11 Nonlinear Models: Steady States

11.1 Introduction

11.2 Equilibria under Perfect Foresight

11.3 Noisy Steady States

11.4 Adaptive Learning for Steady States

11.5 E-Stability and Learning

11.6 Applications

12 Cycles and Sunspot Equilibria

12.1 Introduction

12.2 Overview of Results

12.3 Deterministic Cycles

12.4 Noisy Cycles

12.5 Existence of Sunspot Equilibria

12.6 Learning SSEs

12.7 Global Analysis of Learning Dynamics

12.8 Conclusions

Part V: Further Topics

13 Misspecification and Learning

13.1 Learning in Misspecified Models

13.2 Misspecified Policy Learning

13.3 Conclusions

14 Persistent Learning Dynamics

14.1 Introduction

14.2 Constant-Gain Learning in the Cobweb Model

14.3 Increasing Social Returns and Endogenous Fluctuations

14.4 Sargent’s Inflation Model

14.5 Other Models with Persistent Dynamics

14.6 Conclusions

15 Extensions and Other Approaches

15.1 Models from Computational Intelligence

15.2 Alternative Gain Sequences

15.3 Nonparametric Learning

15.4 Eductive Learning

15.5 Calculation Equilibria

15.6 Adaptively Rational Expectations Equilibria

15.7 ExperimentalWork

15.8 Some Empirical Applications

16 Conclusions

Bibliography

Author Index

Subject Index

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