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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.
Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different robust' policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the US economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies
The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.
This paper introduces time-varying uncertainty into a simple New Keynesian model where the central bank seeks a decision rule that is robust to model misspecification. The paper finds that variation in the central bankXs concern for robustness leads to time-varying, nonnormally distributed impulse responses of output gap, inflation, and the interest rate. These predictions are confirmed by the impulse responses estimated from US quarterly data from 1954 to 2015. Quantitatively, the estimates confirm previous findings that a robust decision maker responds more aggressively than the central bank does empirically.
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.