Robustifying learnability

Tetlow, Robert; Muehlen, Peter von zur

doi:10.1016/j.jedc.2008.06.005

Cited by 8 publications

(3 citation statements)

References 51 publications

(83 reference statements)

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“…This literature can be roughly divided in boundedly rational and rational learning models. The boundedly rational approach describes agents' learning behavior in terms of adaptive learning rules whereby the resulting learning process may or may not converge to the true underlying model (classical contributions include Honkapohja 2001, 2003;Bullard and Mitra 2002;Evans and McGough 2005;Branch and Evans 2007; for more recent contributions see Assenza and Berardi 2009;Tetlow and von zur Muehlen 2009 as well as references therein). In contrast, the rational approach is synonymous with (standard) Bayesian learning which-in line with Doob's consistency theorem-implies convergence to the true value whenever the data is drawn from some i.i.d.…”

Section: Related Literaturementioning

confidence: 99%

Biased Bayesian learning with an application to the risk-free rate puzzle

Ludwig

Zimper

2014

Journal of Economic Dynamics and Control

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Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias that reflects the agent's ambiguity attitudes. By calibrating the standard equilibrium conditions of the consumption based asset pricing model we illustrate that our approach contributes towards a resolution of the risk-free rate puzzle. For a plausible parameterization we obtain a risk-free rate in the range of 35 − 5%. This is 1 − 25% closer to the empirical risk-free rate than according calibrations of the rational expectations model.

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Section: Related Literaturementioning

confidence: 99%

Biased Bayesian learning with an application to the risk-free rate puzzle

Ludwig

Zimper

2014

Journal of Economic Dynamics and Control

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“…In this paper, I do not consider learning by either planner or private agents. An approach to doing so is available in Tetlow and von zur Muehlen (2009) who study robust monetary policy using structured singular value analysis when agents have misspecified models but are engaged in learning under the handicap that their learning of the reduced form of the economy is subject to potentially destabilizing parameter perturbations.…”

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confidence: 99%

Prices and Taxes in a Ramsey Climate Policy Model under Heterogeneous Beliefs and Ambiguity

Muehlen

2022

Economies

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In a Ramsey policy regime, heterogeneity in beliefs about the potential costs of climate change is shown to produce policy ambiguities that alter carbon prices and taxation. Three sources of ambiguity are considered: (i) the private sector is skeptical, with beliefs that are unknown to the government, (ii) private agents have pessimistic doubts about the model, or (iii) the policy authority itself does not trust the extant scientific climate model and fears the worst. These three sources of ambiguity give rise to four potential belief regimes characterized by differentials between the government’s and the private sector’s inter-temporal rates of substitutions, with implications for the prices of carbon and capital, framed in terms of distorted Arrow–Debreu pricing theory that establishes an equivalence between the optimal carbon tax and the permit price of an underlying asset—the government-imposed limit on emissions in economies with cap and trade. This paper shows that in most instances, skeptical beliefs and resulting ambiguities justify higher carbon taxes and lower capital taxes to offset the private sector’s increased myopia compared with rational expectations. Conversely, ambiguities created by worst-case fears in either the private sector or in government tend produce forces in the opposite direction.

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“…An alternative approach, which could be used in this framework, is considered by Tetlow and von zur Muehlen in [14] and Hansen and Sargent in [5]. In these approaches, the policymaker chooses the parameters x 1 and x 2 of the feedback law r…”

Section: Worst-case Inflation Targetingmentioning

confidence: 99%

Stochastic Optimization and Worst-case Decisions

Gülpınar

Rustem

Zakovic

Lecture Notes in Economics and Mathematical Systems

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In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty. We consider expected value optimization of stochastic systems and worst-case robust strategies. Stochastic decision-making involves uncertainty and consequently risk. An important tool to address the inherent error for forecasting uncertainty is worst-case analysis. From the risk management point of view, minimax yields the best strategy determined simultaneously with the worst state of the underlying system. Worst-case analysis is a robust framework for decisions under uncertainty as the actual performance of the decision has a noninferiority property. The significance of robust strategies is increasingly recognized as attitudes towards risk evolve in diverse areas. We present worst-case approach to macroeconomics policy making and financial portfolio management.

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Robustifying learnability

Abstract: In 2006 all ECB publications will feature a motif taken from the €5 banknote. WO R K I N G PA P E R S E R I E S N O. 5 9 3 / F E B R UA RY 2 0 0 6 C O N T E N T S Abstract 4Non-technical summary 5

Cited by 8 publications

References 51 publications

Biased Bayesian learning with an application to the risk-free rate puzzle

Biased Bayesian learning with an application to the risk-free rate puzzle

Prices and Taxes in a Ramsey Climate Policy Model under Heterogeneous Beliefs and Ambiguity

Stochastic Optimization and Worst-case Decisions

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