Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections

TL;DR

This paper compares PCA, ridge regression, and random projections for diffusion index forecasting, showing their consistency under certain conditions and analyzing their performance with real macroeconomic data.

Contribution

It provides a theoretical comparison of PCA, ridge, and random projections in diffusion index models, especially under weak loadings, and evaluates their empirical forecast accuracy.

Findings

All methods are consistent for the conditional mean.

Ridge and random projections have slower convergence with weak loadings and small time dimensions.

Regularization methods may be more robust in practical settings.

Abstract

We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regression and random projections are consistent for the conditional mean under the same assumptions on the strength of the loadings. However, under weaker loadings the convergence rate is lower for ridge and random projections if the time dimension is small relative to the cross-section dimension. We assess the relevance of these findings in an empirical setting by comparing relative forecast accuracy for monthly macroeconomic and financial variables using different window sizes. The findings support the theoretical results, and at the same time show that regularization-based procedures may be more robust in settings not covered by the developed theory.