Factor-Augmented Forecasting in Big Data

53 Pages Posted: 30 Jun 2022 Last revised: 30 Jan 2024

Date Written: June 20, 2022

Abstract

This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined, using 7 factor estimation methods with 13 decision rules that determine the number of factors. The out-of-sample forecasting results show that, first, the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives. This finding is prevalent in many target variables, under different forecasting horizons and models. This significant improvement can be explained by the PLS factor estimation strategy that considers the covariance with the target variable. Second, using a consistently estimated number of factors may not necessarily improve forecasting performance. The greatest predictive gain often derives from decision rules that do not estimate consistently the true number of factors.

Keywords: Factor Models, Forecasting, Partial Least Squares (PLS)

JEL Classification: C22, C23, C53

Suggested Citation

Bae, Juhee, Factor-Augmented Forecasting in Big Data (June 20, 2022). Available at SSRN: https://ssrn.com/abstract=4146589 or http://dx.doi.org/10.2139/ssrn.4146589

Juhee Bae (Contact Author)

University of Glasgow ( email )

United Kingdom

HOME PAGE: http://https://www.juheebae.com

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