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

George Kapetanios*, Simon Price, Garry Young

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Number of pages17
JournalEconometrics and Statistics
Volume7
Early online date21 Feb 2018
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Credit shocks
  • Financial conditions index
  • Forecasting
  • Multivariate partial least squares
  • Targeted data reduction

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