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Econometrics
Volume 10
Issue 2
10.3390/econometrics10020022
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Article

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

by
Duo Qin
1,*,
Sophie van Huellen
1,2,
Qing Chao Wang
3 and
Thanos Moraitis
4
1
Department of Economics, SOAS University of London, 10 Thornhaugh Street, Russell Square, London WC1H 0XG, UK
2
Global Development Institute (GDI), University of Manchester, Oxford Road, Manchester M13 9PL, UK
3
Facebook, UK Limited, London NW1 3FG, UK
4
Department of Economics, University of Massachusetts Amherst, 412 North Pleasant Street, Amherst, MA 01002, USA
*
Author to whom correspondence should be addressed.
Econometrics 2022, 10(2), 22; https://doi.org/10.3390/econometrics10020022
Submission received: 28 September 2018 / Revised: 30 April 2021 / Accepted: 12 April 2022 / Published: 19 April 2022
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
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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.
Keywords: leading indicator; concatenation; forecasting; composite measurement; feature selection; dimensionality reduction leading indicator; concatenation; forecasting; composite measurement; feature selection; dimensionality reduction

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MDPI and ACS Style

Qin, D.; van Huellen, S.; Wang, Q.C.; Moraitis, T. Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data. Econometrics 2022, 10, 22. https://doi.org/10.3390/econometrics10020022

AMA Style

Qin D, van Huellen S, Wang QC, Moraitis T. Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data. Econometrics. 2022; 10(2):22. https://doi.org/10.3390/econometrics10020022

Chicago/Turabian Style

Qin, Duo, Sophie van Huellen, Qing Chao Wang, and Thanos Moraitis. 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data" Econometrics 10, no. 2: 22. https://doi.org/10.3390/econometrics10020022

APA Style

Qin, D., van Huellen, S., Wang, Q. C., & Moraitis, T. (2022). Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data. Econometrics, 10(2), 22. https://doi.org/10.3390/econometrics10020022

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