scholarly journals ROBUST MONETARY POLICY UNDER MODEL UNCERTAINTY IN A SMALL MODEL OF THE U.S. ECONOMY

2002 ◽  
Vol 6 (1) ◽  
pp. 85-110 ◽  
Author(s):  
Alexei Onatski ◽  
James H. Stock
Keyword(s):  
Monetary Policy ◽  
Robust Control ◽  
Model Uncertainty ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Monetary Policies ◽  
Control Techniques ◽  
Decision Theoretic Approach ◽  
Small Model

This paper examines monetary policy in a 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 formulation of model uncertainty used, and we make some suggestions about which formulation is most relevant for monetary policy applications.

scholarly journals ROBUST MONETARY POLICY IN THE NEW KEYNESIAN FRAMEWORK

2008 ◽  
Vol 12 (S1) ◽  
pp. 126-135 ◽  
Author(s):  
KAI LEITEMO ◽  
ULF SÖDERSTRÖM
Keyword(s):  
Monetary Policy ◽  
Robust Control ◽  
Simple Model ◽  
Model Uncertainty ◽  
New Keynesian ◽  
Model Structure ◽  
New Keynesian Model ◽  
Closed Form Solutions ◽  
Control Techniques ◽  
Keynesian Model

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, analyzing 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 the nonrobust policy. Under one particular timing assumption, however, increasing the preference for robustness has no effect on the optimal targeting rule (nor on the economy).

scholarly journals How to Do Things with Words: A Bayesian Approach

2020 ◽  
Vol 68 ◽  
pp. 753-776
Author(s):  
Piotr Gmytrasiewicz
Keyword(s):  
Bayes Theorem ◽  
Theoretic Approach ◽  
Value Iteration ◽  
Bayesian Decision ◽  
Optimality Principle ◽  
Communicative Acts ◽  
Decision Theoretic Approach ◽  
Multi Agent ◽  
Communicative Act ◽  
Belief States

Communication changes the beliefs of the listener and of the speaker. The value of a communicative act stems from the valuable belief states which result from this act. To model this we build on the Interactive POMDP (IPOMDP) framework, which extends POMDPs to allow agents to model others in multi-agent settings, and we include communication that can take place between the agents to formulate Communicative IPOMDPs (CIPOMDPs). We treat communication as a type of action and therefore, decisions regarding communicative acts are based on decision-theoretic planning using the Bellman optimality principle and value iteration, just as they are for all other rational actions. As in any form of planning, the results of actions need to be precisely specified. We use the Bayes’ theorem to derive how agents update their beliefs in CIPOMDPs; updates are due to agents’ actions, observations, messages they send to other agents, and messages they receive from others. The Bayesian decision-theoretic approach frees us from the commonly made assumption of cooperative discourse – we consider agents which are free to be dishonest while communicating and are guided only by their selfish rationality. We use a simple Tiger game to illustrate the belief update, and to show that the ability to rationally communicate allows agents to improve efficiency of their interactions.

scholarly journals Robust Control for a Two DOF Robot Manipulator

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Fatma Massaoudi ◽  
Dorsaf Elleuch ◽  
Tarak Damak
Keyword(s):  
Robust Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Model Uncertainties ◽  
Robot System ◽  
Control Techniques ◽  
Simulation Results ◽  
Manipulator Robot

In this paper, we present robust control techniques applied on a manipulator robot system: modified sliding mode control (MSMC) and backstepping control (BSC). The purpose is to evaluate SMC and BSC performances, taking into account the model uncertainties. Then, the obtained results of MSMC technique are compared with those of the adaptive sliding mode. Both methods have comparable simulation results which show a good quality of robustness. However, simulation results prove that the modified SMC is more robust, mostly under the effect of external variations and uncertainties.

Robust Control of Uncertain Systems With Nonlinearities Using Model-Based Online Robustness Measure

2007 ◽  
Author(s):  
Dirk So¨ffker ◽  
Yan Liu ◽  
Zhiping Qiu ◽  
Fan Zhang ◽  
Peter C. Mu¨ller
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Interval Analysis ◽  
Model Uncertainty ◽  
Control Method ◽  
System Stability ◽  
Theoretic Approach ◽  
Model Based ◽  
Robustness Measure ◽  
Observer Model

In this contribution, the dynamics of linear dynamical systems with nonlinearities or of nonlinear systems with structured uncertainties is controlled based on the stability analysis using the interval-analysis set-theoretic approach and combining the approach with online-optimization of the control parameters. For the online-analysis approach, a high-gain Proportional-Integral-Observer (PI-Observer) is used to estimate the model uncertainty. The estimation can be used as an online-measure of the actual model uncertainty bound which is assumed as known for the online interval analysis. Explicit expressions are given for computing the uncertain linear system stability margin in parameter space, which provides a measure of maximal parameter uncertainties preserving stability of uncertain system around known stable nominal system equilibrium. The robust PI-Observer model-based estimations are used as bounds to evaluate the system stability. The optimization of varied control gains can be used for the optimization of the introduced robustness measure, controlling uncertain nonlinear systems. The results show that the introduced new approach gives better results with respect to robustness and control performance than the classical nonlinear control method and the usual robust control method.

scholarly journals A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 6534-6559 ◽  
Author(s):  
Gustavo da Silva ◽  
Luis Esteves ◽  
Victor Fossaluza ◽  
Rafael Izbicki ◽  
Sergio Wechsler
Keyword(s):  
Hypothesis Testing ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Decision Theoretic Approach

The sample size for a clinical trial: A Bayesian-decision theoretic approach

2001 ◽  
Vol 20 (6) ◽  
pp. 841-858 ◽  
Author(s):  
Jerry Halpern ◽  
Byron Wm. Brown ◽  
John Hornberger
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Decision Theoretic Approach
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