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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.
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.
Using stochastic simulations and stability analysis, the paper compares how different monetary rules perform in a moderately nonlinear model with a time-varying nonaccelerating-inflation-rate-of-unemployment (NAIRU). Rules that perform well in linear models but implicitly embody backward-looking measures of real interest rates (such as conventional Taylor rules) or substantial interest rate smoothing perform very poorly in models with moderate nonlinearities, particularly when policymakers tend to make serially correlated errors in estimating the NAIRU. This challenges the practice of evaluating rules within linear models, in which the consequences of responding myopically to significant overheating are extremely unrealistic.
This thesis analyze the conduct of monetary policy in the presence of uncertainty. By adopting the framework proposed by Hansen and Sargent (2003), we analyze the behaviors of monetary authorities and private agents when faced with various sources of uncertainty, as well as their consequences in terms of macroeconomic performances. Our work shows that, in order to guard against the possibly catastrophic results of the worst-case scenario, central bankers have to react in an active manner, by manipulating the interest rate. However, in an open economy, the magnitude of this adjustment decreases with the degree of openness. Also, greater transparency of the central bank's objectives, by reducing preference uncertainty, will attenuate the variations of macroeconomic variables that follow the consideration of possible erroneous specifications. It is thus advisable to reveal informations about the central bankers' preferences, including their own estimates of the degree of model uncertainty.
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
"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