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

Timothy Cogley (), Bianca De Paoli (), Christian Matthes, Kalin Nikolov and Anthony Yates ()

No 414, Bank of England working papers from Bank of England

Abstract: This paper undertakes a Bayesian analysis of optimal monetary policy for the United Kingdom. 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 optimised for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimised 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 stabilisation in forward-looking models.

Pages: 74 pages
Date: 2011-03-02
New Economics Papers: this item is included in nep-cba, nep-mac and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
https://www.bankofengland.co.uk/-/media/boe/files/ ... odel-uncertainty.pdf Full text (application/pdf)

Related works:
Journal Article: A Bayesian approach to optimal monetary policy with parameter and model uncertainty (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0414

Access Statistics for this paper

More papers in Bank of England working papers from Bank of England Bank of England, Threadneedle Street, London, EC2R 8AH. Contact information at EDIRC.
Bibliographic data for series maintained by Digital Media Team ().