Forecasting with Partial Least Squares Using Many Predictors

48 Pages Posted: 19 Oct 2022 Last revised: 30 Nov 2023

See all articles by Seung C. Ahn

Seung C. Ahn

Arizona State University (ASU) - Economics Department

Juhee Bae

University of Glasgow

Date Written: February 12, 2022

Abstract

This paper considers a forecasting model in which a target variable is a linear function of K latent factors among many predictors (N). The target variable is forecasted by regression with factors generated by the Partial Least Squares (PLS) method. Our asymptotic analysis shows that the optimal number (q*) of PLS factors for forecasting can be much smaller than K. Using more than q* PLS factors can cause an over-fitting problem, which deteriorates the out-of-sample forecasting accuracy while yielding high in-sample fit. Our Monte Carlo simulation results confirm these asymptotic results. Furthermore, our simulation exercises and topical empirical analysis indicate that using q* PLS factors is not necessarily desirable in practice unless very large samples are used. Using smaller than q* PLS factors often produces more accurate forecasting results. Especially, a single PLS factor very often outperforms q* PLS factors, even when q*>1.

Keywords: Partial Least Squares, Factors, Forecasting

JEL Classification: C51, C53, C55

Suggested Citation

Ahn, Seung C. and Bae, Juhee, Forecasting with Partial Least Squares Using Many Predictors (February 12, 2022). Available at SSRN: https://ssrn.com/abstract=4248450 or http://dx.doi.org/10.2139/ssrn.4248450

Seung C. Ahn

Arizona State University (ASU) - Economics Department ( email )

Tempe, AZ 85287-3806
United States

Juhee Bae (Contact Author)

University of Glasgow ( email )

United Kingdom

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

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