A UK financial conditions index using targeted data reduction: Forecasting and structural identification

George Kapetanios, Simon Price and Garry Young

Econometrics and Statistics, 2018, vol. 7, issue C, 1-17

Abstract: A financial conditions index (FCI) is designed to summarise the state of financial markets. Two are constructed with UK data. The first is the first principal component of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic variables weighted by the joint covariance with a subset of the financial indicators (a set of spreads), using multivariate partial least squares, again using the first factor. The resulting FCIs are broadly similar. They both have some forecasting power for monthly GDP in a quasi-real-time recursive evaluation from 2011 to 2014 and outperform an FCI produced by Goldman Sachs. A second factor, that may be interpreted as a monetary conditions index, adds further forecast power, while third factors have a mixed effect on performance. The FCIs are used to improve identification of credit supply shocks in an SVAR. The main effects relative to an SVAR excluding an FCI of the (adverse) credit shock IRFs are to make the positive impact on inflation more precise and to reveal an increased positive impact on spreads.

Keywords: Forecasting; Financial conditions index; Targeted data reduction; Multivariate partial least squares; Credit shocks (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: A UK financial conditions index using targeted data reduction: forecasting and structural identification (2017)
Working Paper: A UK financial conditions index using targeted data reduction: forecasting and structural identification (2017)
Working Paper: A UK financial conditions index using targeted data reduction: forecasting and structural identification (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:7:y:2018:i:c:p:1-17

DOI: 10.1016/j.ecosta.2017.12.002

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