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.

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.

The paper considers gains from international economic policy coordination when there is uncertainty concerning the functioning of the world economy, but also learning about the “true” model on the part of policymakers. The paper reports estimates of plausible alternative versions of a standard, two-country model. Activist policy (either coordinated or uncoordinated) may produce large welfare losses in the absence of learning, if policymakers believe in the wrong model; hence exogenous money targets and freely flexible exchange rates may be best. However, model learning (from observations on macroeconomic variables) causes coordinated policies to dominate activist uncoordinated policies or exogenous money targets.

Model uncertainty has the potential to change importantly how monetary policy should be conducted, making it an issue that central banks cannot ignore. In this paper, I use a standard new Keynesian business cycle model to analyze the behavior of a central bank that conducts policy with discretion while fearing that its model is misspecified. I begin by showing how to solve linear-quadratic robust Markov-perfect Stackelberg problems where the leader fears that private agents form expectations using the misspecified model. Next, I exploit the connection between robust control and uncertainty aversion to present and interpret my results in terms of the distorted beliefs held by the central bank, households, and firms. My main results are as follows. First, the central bank's pessimism leads it to forecast future outcomes using an expectations operator that, relative to rational expectations, assigns greater probability to extreme inflation and consumption outcomes. Second, the central bank's skepticism about its model causes it to move forcefully to stabilize inflation following shocks. Finally, even in the absence of misspecification, policy loss can be improved if the central bank implements a robust policy.

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.