Working Paper

Forecasting with Sufficient Dimension Reductions

Abstract: Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y it is. Most existing methods first reduce the predictors and then forecast y in independent phases of the modeling process. In this paper we present an alternative and potentially more attractive alternative: summarizing x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. These y-targeted reductions of the predictors are obtained using Sufficient Dimension Reduction techniques. We show in simulations and real data analysis that forecasting models based on sufficient reductions have the potential of significantly improved performance.

Keywords: Diffusion Index; Dimension Reduction; Factor Models; Forecasting; Partial Least Squares; Principal Components;

JEL Classification: C32; C53; C55; E17;

https://doi.org/10.17016/FEDS.2015.074

Bibliographic Information

Provider: Board of Governors of the Federal Reserve System (U.S.)

Part of Series: Finance and Economics Discussion Series

Publication Date: 2015-09-14

Number: 2015-74

Pages: 50 pages