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This paper proposes a model uncertainty framework that accounts for the uncertainty about both the specification of the Phillips curve and the identification assumption to be used for parameter estimation. More specifically, the paper extends the framework employed by Cogley and Sargent (2005) to incorporate uncertainty over the direction of fit of the Phillips curve. I first study the evolution of the model posterior probabilities, which can be interpreted as a measure of the econometrician's real-time beliefs over the prevailing model of the Phillips curve. I then characterize the optimal policy rule within each model, and I analyze alternative policy recommendations that incorporate model uncertainty. As expected, different directions of fit of the same model of the Phillips curve imply very different optimal policy choices, with the "Classical" specifications typically suggesting low and stable optimal inflation rates. I also find that allowing rational agents to incorporate model uncertainty in their expectations does not change the optimal or robust policies. On the other hand, I show that the models' fit to the data and the robust policy recommendations are affected by the specific price index that is used to measure in inflation.
"What tools are available for setting and analyzing monetary policy? World-renowned contributors examine recent evidence on subjects as varied as price-setting, inflation persistence, the private sector's formation of inflation expectations, and the monetary policy transmission mechanism. Stopping short of advocating conclusions about the ideal conduct of policy, the authors focus instead on analytical methods and the changing interactions among the ingredients and properties that inform monetary models. The influences between economic performance and monetary policy regimes can be both grand and muted, and this volume clarifies the present state of this continually evolving relationship." [source : 4e de couv.].
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.
Model Validation and Uncertainty Quantification, Volume 3. Proceedings of the 33rd IMAC, A Conference and Exposition on Balancing Simulation and Testing, 2015, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Uncertainty Quantification & Model Validation Uncertainty Propagation in Structural Dynamics Bayesian & Markov Chain Monte Carlo Methods Practical Applications of MVUQ Advances in MVUQ & Model Updating