Optimal Monetary Policy under Uncertainty, Second Edition
Optimal Monetary Policy under Uncertainty, Second Edition

Author: Richard T. Froyen

Publisher: Edward Elgar Publishing

Published: 2019

Total Pages: 466

ISBN-13: 1784717193

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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.

Optimal Monetary Policy Under Uncertainty in DSGE Models
Optimal Monetary Policy Under Uncertainty in DSGE Models

Author: Lars E. O. Svensson

Publisher:

Published: 2008

Total Pages: 27

ISBN-13:

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We study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium models. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, approximating the uncertainty by different discrete modes in a Markov chain, and by taking mode-dependent linear-quadratic approximations of the underlying model. This allows us to apply a powerful methodology with convenient solution algorithms that we have developed. We apply our methods to a benchmark New Keynesian model, analyzing how policy is affected by uncertainty, and how learning and active experimentation affect policy and losses.

Optimal Fiscal Adjustment under Uncertainty
Optimal Fiscal Adjustment under Uncertainty

Author: Rossen Rozenov

Publisher: International Monetary Fund

Published: 2016-03-17

Total Pages: 51

ISBN-13: 1475521790

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The paper offers a non-probabilistic framework for representation of uncertainty in the context of a simple linear-quadratic model of fiscal adjustment. Instead of treating model disturbances as random variables with known probability distributions, it is only assumed that they belong to some pre-specified compact set. Such an approach is appropriate when the decision maker does not have enough information to form probabilistic beliefs or when considerations for robustness are important. Solution of the model in the minimax sense when disturbance sets are ellipsoids is obtained and the application of the method is illustrated using the example of Portugal.