Probability models and robust policy rules

Paul Levine (), Peter McAdam () and Joseph Pearlman

European Economic Review, 2012, vol. 56, issue 2, 246-262

Abstract: We consider Sims's (2008) argument that robust policy making requires that policy models be treated as “probability models”. In a welfare-based setting, we estimate by Bayesian methods a number of variants of a New Keynesian macroeconomic model and use both the model odds and posterior densities to design robust interest rate rules consisting of an inflation-forecast-based rule and a wage-targeting one. Each are shown to have distinct robustness qualities and distinct implications for the probability-models approach. To ensure feasible policy, we further impose that rules are stable, determinate and lower-bound compatible. Our results have important implications for the design, evaluation and analysis of the probability models approach to robust monetary policy making.

Keywords: Probability models; Interest-rate rules; Robustness; Bayes theorem; Structured uncertainty; Markov Chain Monte Carlo; Zero lower bound (search for similar items in EconPapers)
JEL-codes: E37 E52 E58 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eecrev:v:56:y:2012:i:2:p:246-262

DOI: 10.1016/j.euroecorev.2011.08.005

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