Bayesian And Adaptive Optimal Policy Under Model Uncertainty
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| Author | : Lars E. O. Svensson |
| Publisher | : |
| Total Pages | : 48 |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
| Author | : Lars E. O. Svensson |
| Publisher | : |
| Total Pages | : 60 |
| Release | : 2007 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
| Author | : Lars E.O. Svensson |
| Publisher | : |
| Total Pages | : 46 |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
| Author | : Oliver Sauter |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 223 |
| Release | : 2014-01-31 |
| Genre | : Political Science |
| ISBN | : 365804974X |
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.
| Author | : Benjamin M. Friedman |
| Publisher | : Newnes |
| Total Pages | : 1729 |
| Release | : 1990 |
| Genre | : Business & Economics |
| ISBN | : 0444534709 |
How have monetary policies matured during the last decade? The recent downturn in economies worldwide have put monetary policies in a new spotlight. In addition to their investigations of new tools, models, and assumptions, they look carefully at recent evidence on subjects as varied as price-setting, inflation persistence, the private sector's formation of inflation expectations, and the monetary policy transmission mechanism. They also reexamine standard presumptions about the rationality of asset markets and other fundamentals. Stopping short of advocating conclusions about the ideal conduct of policy, the authors focus instead on analytical methods and the changing interactions among the ingredients and properties that inform monetary models. The influences between economic performance and monetary policy regimes can be both grand and muted, and this volume clarifies the present state of this continually evolving relationship. Presents extensive coverage of monetary policy theories with an eye toward questions raised by the recent financial crisis Explores the policies and practices used in formulating and transmitting monetary policies Questions fiscal-monetary connnections and encourages new thinking about the business cycle itself Observes changes in the formulation of monetary policies over the last 25 years
| Author | : Benjamin M. Friedman |
| Publisher | : Elsevier |
| Total Pages | : 971 |
| Release | : 2010-11-16 |
| Genre | : Business & Economics |
| ISBN | : 0444534547 |
"What tools are available for setting and analyzing monetary policy? World-renowned contributors examine recent evidence on subjects as varied as price-setting, inflation persistence, the private sector's formation of inflation expectations, and the monetary policy transmission mechanism. Stopping short of advocating conclusions about the ideal conduct of policy, the authors focus instead on analytical methods and the changing interactions among the ingredients and properties that inform monetary models. The influences between economic performance and monetary policy regimes can be both grand and muted, and this volume clarifies the present state of this continually evolving relationship." [source : 4e de couv.].
| Author | : Karl Schmedders |
| Publisher | : Newnes |
| Total Pages | : 680 |
| Release | : 2013-12-31 |
| Genre | : Business & Economics |
| ISBN | : 0080931782 |
Handbook of Computational Economics summarizes recent advances in economic thought, revealing some of the potential offered by modern computational methods. With computational power increasing in hardware and algorithms, many economists are closing the gap between economic practice and the frontiers of computational mathematics. In their efforts to accelerate the incorporation of computational power into mainstream research, contributors to this volume update the improvements in algorithms that have sharpened econometric tools, solution methods for dynamic optimization and equilibrium models, and applications to public finance, macroeconomics, and auctions. They also cover the switch to massive parallelism in the creation of more powerful computers, with advances in the development of high-power and high-throughput computing. Much more can be done to expand the value of computational modeling in economics. In conjunction with volume one (1996) and volume two (2006), this volume offers a remarkable picture of the recent development of economics as a science as well as an exciting preview of its future potential. Samples different styles and approaches, reflecting the breadth of computational economics as practiced today Focuses on problems with few well-developed solutions in the literature of other disciplines Emphasizes the potential for increasing the value of computational modeling in economics
| Author | : Luis-Felipe Zanna |
| Publisher | : International Monetary Fund |
| Total Pages | : 47 |
| Release | : 2010-12-01 |
| Genre | : Business & Economics |
| ISBN | : 145521177X |
Empirical evidence suggests that goods are highly heterogeneous with respect to the degree of price rigidity. We develop a DSGE model featuring heterogeneous nominal rigidities across two sectors to study the equilibrium determinacy and stability under adaptive learning for interest rate rules that respond to inflation measures differing in their degree of price stickiness. We find that rules responding to headline inflation measures that assign a positive weight to the inflation of the sector with low price stickiness are more prone to generate macroeconomic instability than rules that respond exclusively to the inflation of the sector with high price stickiness. By this we mean that they are more prone to induce non-learnable fundamental-driven equilibria, learnable self-fulfilling expectations equilibria, and equilibria where fluctuations are unbounded. We discuss how our results depend on the elasticity of substitution across goods, the degree of heterogeneity in price rigidity, as well as on the timing of the rule.
| Author | : J.K. Sengupta |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 295 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 3642701639 |
Understanding the stochastic enviornment is as much important to the manager as to the economist. From production and marketing to financial management, a manager has to assess various costs imposed by uncertainty. The economist analyzes the role of incomplete and too often imperfect information structures on the optimal decisions made by a firm. The need for understanding the role of uncertainty in quantitative decision models, both in economics and management science provide the basic motivation of this monograph. The stochastic environment is analyzed here in terms of the following specific models of optimization: linear and quadratic models, linear programming, control theory and dynamic programming. Uncertainty is introduced here through the para meters, the constraints, and the objective function and its impact evaluated. Specifically recent developments in applied research are emphasized, so that they can help the decision-maker arrive at a solution which has some desirable charac teristics like robustness, stability and cautiousness. Mathematical treatment is kept at a fairly elementary level and applied as pects are emphasized much more than theory. Moreover, an attempt is made to in corporate the economic theory of uncertainty into the stochastic theory of opera tions research. Methods of optimal decision rules illustrated he re are applicable in three broad areas: (a) applied economic models in resource allocation and economic planning, (b) operations research models involving portfolio analysis and stochastic linear programming and (c) systems science models in stochastic control and adaptive behavior.
| Author | : Moshe Dror |
| Publisher | : Springer |
| Total Pages | : 770 |
| Release | : 2019-11-05 |
| Genre | : Mathematics |
| ISBN | : 0306481022 |
Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.
