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Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data

Author

Listed:
  • Duo Qin

    (Department of Economics, SOAS University of London, 10 Thornhaugh Street, Russell Square, London WC1H 0XG, UK)

  • Sophie van Huellen

    (Department of Economics, SOAS University of London, 10 Thornhaugh Street, Russell Square, London WC1H 0XG, UK
    Global Development Institute (GDI), University of Manchester, Oxford Road, Manchester M13 9PL, UK)

  • Qing Chao Wang

    (Facebook, UK Limited, London NW1 3FG, UK)

  • Thanos Moraitis

    (Department of Economics, University of Massachusetts Amherst, 412 North Pleasant Street, Amherst, MA 01002, USA)

Abstract

Aggregate financial conditions indices (FCIs) are constructed to fulfil two aims: (i) The FCIs should resemble non-model-based composite indices in that their composition is adequately invariant for concatenation during regular updates; (ii) the concatenated FCIs should outperform financial variables conventionally used as leading indicators in macro models. Both aims are shown to be attainable once an algorithmic modelling route is adopted to combine leading indicator modelling with the principles of partial least-squares (PLS) modelling, supervised dimensionality reduction, and backward dynamic selection. Pilot results using US data confirm the traditional wisdom that financial imbalances are more likely to induce macro impacts than routine market volatilities. They also shed light on why the popular route of principal-component based factor analysis is ill-suited for the two aims.

Suggested Citation

  • Duo Qin & Sophie van Huellen & Qing Chao Wang & Thanos Moraitis, 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data," Econometrics, MDPI, vol. 10(2), pages 1-22, April.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:2:p:22-:d:797393
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