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This paper examines monetary policy in Rudebusch and Svensson's (1999) two equation macroeconomic model when the policymaker recognizes that the model is an approximation and is uncertain about the quality of that approximation. It is argued that the minimax approach of robust control provides a general and tractable alternative to the conventional Bayesian decision theoretic approach. Robust control techniques are used to construct robust monetary policies. In most (but not all) cases, these robust policies are more aggressive than the optimal policies absent model uncertainty. The specific robust policies depend strongly on the formation of model uncertainty used, and we make some suggestions about which formulation is most relevant for monetary policy applications.
This book provides a thorough survey of the model-based literature on optimal monetary in a stochastic setting. The survey begins with the literature of the 1970s which focused on the information problem in policy design and extends to the New Keynesian approach of the 1990s which centered on evaluating alternative targeting strategies. New to the second edition is consideration of research since the world financial crisis on the role of financial markets and institutions in the conduct of monetary policy.
In the last few years, several articles have been devoted to the study of model uncertainty in the New Keynesian model using robust control methods. Most studies have focused on how to design a robust monetary policy to take model uncertainty more seriously. Little attention has, however, been given to expectation formation under such a robust monetary policy. The purpose of this study is to explore the expectational stability under robust monetary policy when private expectations are formed by the adaptive learning technology. We find that the economy is determinate and stable under learning if (i) private agents' expectations are observable to the central bank and appropriately incorporated into its optimal policy rules, and (ii) the central bank's preference for robustness is sufficiently weak. It follows that it is important for the central bank to consider expectational stability when it implements a robust monetary policy.
We provide a framework for analyzing the choice between optimal and robust monetary policy rules in the presence of paradigm uncertainty. We first discuss the conditions on uncertainty that render a robust rule preferable to an optimal rule. Second, we show how the degree of risk aversion of the policymaker increases the region in which the robust rule is preferred.
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.
Casting a wide net in this, their second edition, Froyen and Guender provide coverage of the model-based literature on optimal monetary policy in the presence of uncertainty, with both open- and closed-economy frameworks considered. The authors have grounded New Keynesian research of the 1990s and 2000s in the literature of the 1970s, which viewed optimal policy as primarily a question of the optimal use of information, and studies in the 1980s that gave primacy to time inconsistency problems. The Global Financial Crisis of 2007-09 led to the recognition that financial markets and institutions required greater attention in policy modelling. Herein, the authors provide a thorough survey of the post-crisis literature that resulted from this recognition.Researchers in academia and at central banks, students and policy makers will value the wide scope of coverage provided in this examination, leading them to a better understanding of issues such as discretion versus commitment, target versus instrument rules, policy in closed versus open economies and the proper mandate for central banks, including the relationship between interest rate policy and macro-prudential instruments.
The scope of this volume is primarily to analyze from different methodological perspectives similar valuation and optimization problems arising in financial applications, aimed at facilitating a theoretical and computational integration between methods largely regarded as alternatives. Increasingly in recent years, financial management problems such as strategic asset allocation, asset-liability management, as well as asset pricing problems, have been presented in the literature adopting formulation and solution approaches rooted in stochastic programming, robust optimization, stochastic dynamic programming (including approximate SDP) methods, as well as policy rule optimization, heuristic approaches and others. The aim of the volume is to facilitate the comprehension of the modeling and methodological potentials of those methods, thus their common assumptions and peculiarities, relying on similar financial problems. The volume will address different valuation problems common in finance related to: asset pricing, optimal portfolio management, risk measurement, risk control and asset-liability management. The volume features chapters of theoretical and practical relevance clarifying recent advances in the associated applied field from different standpoints, relying on similar valuation problems and, as mentioned, facilitating a mutual and beneficial methodological and theoretical knowledge transfer. The distinctive aspects of the volume can be summarized as follows: Strong benchmarking philosophy, with contributors explicitly asked to underline current limits and desirable developments in their areas. Theoretical contributions, aimed at advancing the state-of-the-art in the given domain with a clear potential for applications The inclusion of an algorithmic-computational discussion of issues arising on similar valuation problems across different methods. Variety of applications: rarely is it possible within a single volume to consider and analyze different, and possibly competing, alternative optimization techniques applied to well-identified financial valuation problems. Clear definition of the current state-of-the-art in each methodological and applied area to facilitate future research directions.
Oliver Sauter analyzes three aspects of monetary policy under uncertainty. First he shows that the terms risk and uncertainty are often wrongly used as synonyms despite their different meanings. The second aspect is the proper examination and incorporation of uncertainty into a monetary policy framework. The author undertakes systematization with a closer look at each identified form of uncertainty. Thirdly, he focuses on the quantification of uncertainty from two different perspectives, either from a market perspective or from a central bank perspective.