Optimal Policy Under Model Uncertainty
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Author | : Lars E. O. Svensson |
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Total Pages | : 60 |
Release | : 2007 |
Genre | : Bayesian statistical decision theory |
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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 | : Anna Orlik |
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Release | : 2013 |
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Release | : 2007 |
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In this paper we propose a novel methodology to analyze optimal policies under model uncertainty in micro-founded macroeconomic models. As an application we assess the relevant sources of uncertainty for the optimal conduct of monetary policy within (parameter uncertainty) and across models (specification uncertainty) using EU 13 data. Parameter uncertainty matters only if the zero bound on interest rates is explicitly taken into account. In any case, optimal monetary policy is highly sensitive with respect to specification uncertainty implying substantial welfare gains of a robust-optimal rule that incorporates this risk. -- Optimal monetary policy ; model uncertainty ; Bayesian model estimation
Author | : Lars E.O. Svensson |
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Total Pages | : 46 |
Release | : 2007 |
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Author | : Alexei Onatski |
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Total Pages | : 33 |
Release | : 2000 |
Genre | : Macroeconomics |
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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.
Author | : Richard T. Froyen |
Publisher | : Edward Elgar Publishing |
Total Pages | : 466 |
Release | : 2019 |
Genre | : Mathematical optimization |
ISBN | : 1784717193 |
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.
Author | : Marc Paolo Giannoni |
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Total Pages | : 388 |
Release | : 2001 |
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Author | : Carlo A. Favero |
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Total Pages | : 0 |
Release | : 2001 |
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Author | : Michael Funke |
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Total Pages | : 15 |
Release | : 2007 |
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Author | : Lars E. O. Svensson |
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Total Pages | : 84 |
Release | : 2005 |
Genre | : Economic forecasting |
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"We examine optimal and other monetary policies in a linear-quadratic setup with a relatively general form of model uncertainty, so-called Markov jump-linear-quadratic systems extended to include forward-looking variables. The form of model uncertainty our framework encompasses includes: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more complex structural uncertainty about very different models, for instance, backward- and forward-looking models; time-varying central-bank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary policy functions. This allows us to compute and plot consistent distribution forecasts---fan charts---of target variables and instruments. Our methods hence extend certainty equivalence and "mean forecast targeting" to more general certainty non-equivalence and "distribution forecast targeting.""--National Bureau of Economic Research web site