Bibliographic Information

Robustness

Lars Peter Hansen, Thomas J. Sargent

Princeton University Press, c2008

  • : cloth
  • : pbk

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Includes bibliographical references (p. 413-425) and indexes

Description and Table of Contents

Description

The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.

Table of Contents

Preface xv Acknowledgments xvii Part I: Motivation and main ideas Chapter 1: Introduction 3 Chapter 2: Basic ideas and methods 25 Chapter 3: A stochastic formulation 53 Part II: Standard control and filtering Chapter 4: Linear control theory 67 Chapter 5: The Kalman filter 103 Part III: Robust control Chapter 6: Static multiplier and constraint games 119 Chapter 7: Time domain games for attaining robustness 139 Chapter 8: Frequency domain games and criteria for robustness 173 Chapter 9: Calibrating misspecification fears with detection error probabilities 213 Chapter 10: A permanent income model 223 Part IV: Multi-agent problems Chapter 11: Competitive equilibria without robustness 253 Chapter 12: Competitive equilibria with robustness 271 Chapter 13: Asset pricing 295 Chapter 14: Risk sensitivity, model uncertainty, and asset pricing 307 Chapter 15: Markov perfect equilibria with robustness 327 Chapter 16: Robustness in forward-looking models 333 Part V: Robust estimation and filtering Chapter 17: Robust filtering with commitment 359 Chapter 18: Robust filtering without commitment 383 Part VI: Extensions Chapter 19: Alternative approaches 403 References 413 Index 427 Author Index 431 Matlab Index 435

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