Download Free Robust Monetary Policy Under Model Uncertainty Book in PDF and EPUB Free Download. You can read online Robust Monetary Policy Under Model Uncertainty and write the review.

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.
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.
We provide a framework for analyzing the choice between optimal and robust monetary policy rules in the presence of paradigm uncertainty. We first discuss the conditions on uncertainty that render a robust rule preferable to an optimal rule. Second, we show how the degree of risk aversion of the policymaker increases the region in which the robust rule is preferred.
In this paper, we study the impact of central bank opacity on macroeconomic performances in a new Keynesian framework with model uncertainty using robust control techniques. We identify a new source of central bank opacity, which refers to the lack of information about the central bank's preference for robustness in the sense of Hansen and Sargent. We find closed-form solutions for the robust control problem, analysing the impact of the lack of transparency about the central bank's preferences for robustness. We show that an increased transparency about the central bank's preference for robustness makes monetary policy respond less aggressively to cost-push shocks, thus reducing the inflation and output gap variability. As a consequence, inflation and output gap are less volatile than under central bank opacity about its preference for robustness.
We study the effects of model uncertainty in a simple New-Keynesian model using robust control techniques. Due to the simple model structure, we are able to find closed-form solutions for the robust control problem, analysing both instrument rules and targeting rules under different timing assumptions. In all cases but one, an increased preference for robustness makes monetary policy respond more aggressively to cost shocks but leaves the response to demand shocks unchanged. As a consequence, inflation is less volatile and output is more volatile than under a non-robust policy. Under one particular timing assumption, however, increasing the preference for robustness has no effect on the optimal targeting rule (nor on the economy).
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.