A time varying parameter structural model of the UK economy

Katerina Petrova, George Kapetanios, Riccardo M. Masolo and Matt Waldron ()

No 677, Bank of England working papers from Bank of England

Abstract: e estimate a time varying parameter structural macroeconomic model of the UK economy, using a Bayesian local likelihood methodology. This enables us to estimate a large open-economy DSGE model over a sample that comprises several different regimes and an incomplete set of data. Our estimation identifies a gradual shift to a monetary policy regime characterised by a marked increase in the responsiveness of monetary policy to inflation alongside a decrease in the level of trend inflation down to the 2% target level. The time varying model also performs remarkably well in forecasting and delivers statistically significant accuracy improvements for most variables and horizons in both point and density forecast performance compared to the standard fixed parameter version.

Keywords: DSGE models; Bayesian methods; local likelihood; time varying parameters; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C53 E27 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2017-09-08
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.bankofengland.co.uk/-/media/boe/files/ ... 8F9C4ED4BEED346AAD92 Full text (application/pdf)

Related works:
Journal Article: A time-varying parameter structural model of the UK economy (2019)
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:0677

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 ().