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Predictive Macro-Impacts of PLS-based Financial Conditions Indices: An Application to the USA

Author

Listed:
  • Duo Qin

    (Department of Economics, SOAS University of London, UK)

  • Qingchao Wang

    (Department of Economics, SOAS University of London, UK)

Abstract

This investigation seeks to construct financial conditions indices (FCIs) by the partial least squares (PLS) method with the aims (i) that the FCIs should outperform interest rate, which is conventionally used in small VAR (Vector Auto-Regression) models to present the predictive macro-impacts of the financial markets, and (ii) that the FCIs are adequately invariant during regular updates to resemble non-model based aggregate indices. Both aims are shown to be attainable as long as the FCIs are tailor-made with carefully selected components and suitably targeted macro variables of forecasting interest. The positive outcome sheds light on why the widely used principal component analysis (PCA) approach is ill-suited to the tasks here whereas why the PLS route promises a fruitful way forward.

Suggested Citation

  • Duo Qin & Qingchao Wang, 2016. "Predictive Macro-Impacts of PLS-based Financial Conditions Indices: An Application to the USA," Working Papers 201, Department of Economics, SOAS University of London, UK.
    • Handle: RePEc:soa:wpaper:201
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    References listed on IDEAS

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    More about this item

    Keywords

    Partial Least Squares; financial conditions index; concatenation; forecasting;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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