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Probability models and robust policy rules

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  • Levine, Paul
  • McAdam, Peter
  • Pearlman, Joseph

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.

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Bibliographic Info

Article provided by Elsevier in its journal European Economic Review.

Volume (Year): 56 (2012)
Issue (Month): 2 ()
Pages: 246-262

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Handle: RePEc:eee:eecrev:v:56:y:2012:i:2:p:246-262

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Web page: http://www.elsevier.com/locate/eer

Related research

Keywords: Probability models; Interest-rate rules; Robustness; Bayes theorem; Structured uncertainty; Markov Chain Monte Carlo; Zero lower bound;

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References

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Citations

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Cited by:
  1. Faria, Joao Ricardo & McAdam, Peter, 2012. "Anticipation of future consumption: a monetary perspective," Working Paper Series 1448, European Central Bank.
  2. Cristiano Cantore & Paul Levine & Giovanni Melina, 2011. "A Fiscal Stimulus and Jobless Recovery," School of Economics Discussion Papers 1111, School of Economics, University of Surrey.
  3. Paul Levine, 2012. "Policy focus: Monetary policy in an uncertain world: probability models and the design of robust monetary rules," Indian Growth and Development Review, Emerald Group Publishing, vol. 5(1), pages 70-88, April.

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