Macroeconomic data transformations matter

Philippe Goulet Coulombe, Maxime Leroux, Dalibor Stevanovic and Stéphane Surprenant

International Journal of Forecasting, 2021, vol. 37, issue 4, 1338-1354

Abstract: In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization – explicit or implicit – embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found that traditional factors should almost always be included as predictors and moving average rotations of the data can provide important gains for various forecasting targets. Also, we note that while predicting directly the average growth rate is equivalent to averaging separate horizon forecasts when using OLS-based techniques, the latter can substantially improve on the former when regularization and/or nonparametric nonlinearities are involved.

Keywords: Machine learning; Big data; Forecasting; Feature engineering; Regularization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207021000777
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Macroeconomic Data Transformations Matter (2021)
Working Paper: Macroeconomic Data Transformations Matter (2021)
Working Paper: Macroeconomic Data Transformations Matter (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:4:p:1338-1354

DOI: 10.1016/j.ijforecast.2021.05.005

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().