A Bayesian approach to optimal monetary policy with parameter and model uncertainty

Timothy Cogley, Bianca De Paoli, Christian Matthes, Kalin Nikolov, Tony Yates

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetary-policy models that include both forward- and backward-looking representations as well as large- and small-scale models. We find an optimal simple Taylor-type rule that accounts for both model and parameter uncertainty. For the most part, backward-looking models are highly fault tolerant with respect to policies optimized for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimized for backward-looking representations. In addition, backward-looking models often have lower posterior probabilities than forward-looking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forward-looking models.

    Original languageEnglish (US)
    Pages (from-to)2186-2212
    Number of pages27
    JournalJournal of Economic Dynamics and Control
    Volume35
    Issue number12
    DOIs
    StatePublished - Dec 2011

    Keywords

    • Bayesian analysis
    • Monetary policy
    • Quantitative policy modeling
    • Statistical decision theory

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Control and Optimization
    • Applied Mathematics

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