Forecasting With Dynamic Factor Models Estimated by Partial Least Squares - UTU Research Portal

A1 Refereed original research article in a scientific journal

Forecasting With Dynamic Factor Models Estimated by Partial Least Squares

Authors: Rauhala, Samuel

Publisher: Wiley

Publication year: 2026

Journal: Journal of Forecasting

Article number: for.70158

ISSN: 1099-131X

eISSN: 0277-6693

DOI: https://doi.org/10.1002/for.70158

Publication's open availability at the time of reporting: Open Access

Publication channel's open availability : Partially Open Access publication channel

Web address : https://doi.org/10.1002/for.70158

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523225592

Self-archived copy's licence: CC BY

Self-archived copy's version: Publisher`s PDF

Abstract

Dynamic factor models (DFMs) have found great success in nowcasting and short-term macroeconomic forecasting when incorporating large sets of predictive information. The factor loadings are typically estimated cross-sectionally with principal component analysis (PCA) or maximum likelihood (ML), which ignore whether the factors have predictive power. We suggest two novel alternative approaches using partial least squares to estimate large vector autoregressions (VARs) and DFMs, which take the dynamic dependencies better into account. Our Monte Carlo simulations and forecasting results for the Finnish GDP growth show that these methods generally perform on par with and under certain conditions better than the existing approaches.

Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.

Journal of Forecasting - 2026 - Rauhala - Forecasting With Dynamic Factor Models Estimated by Partial Least Squares.pdf

Funding information in the publication:
The author would like to thank Petteri Juvonen, Juho Koistinen, Markku Lanne, Henri Nyberg, Joni Virta, and seminar participants at the Bank of Finland (2024), Nordic Econometric Meeting (Bergen 2024), CFE 2024 (London), and Helsinki Graduate School of Economics (2024). The financial support from the OP Group Research Foundation (grant 20230116), the Foundation for Economic Education (Liikesivistysrahasto, grant 220246), the Turku University Foundation (Turun Yliopistosaatio, grant 081875), and The Finnish Doctoral Program Network in Artificial Intelligence, AI-DOC (decision number VN/3137/2024-OKM-6) is gratefully acknowledged. Special thanks are due to the Bank of Finland for the data used in Section 5 and research cooperation (research visit in the autumn 2024). Open access publishing facilitated by Turun yliopisto, as part of the Wiley - FinELib agreement.