Working Paper: CEPR ID: DP7446
Authors: Andrea Carriero; George Kapetanios; Massimiliano Marcellino
Abstract: The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large scale Bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank Bayesian VAR of Geweke (1996). We find that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the ground to use large scale reduced rank models for empirical analysis.
Keywords: Bayesian VARs; Factor Models; Forecasting; Reduced Rank
JEL Codes: C11; C13; C33; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| combination of shrinkage and rank reduction (C51) | forecast accuracy (C53) |
| RRP and BRR models (C59) | forecast accuracy (C53) |
| methodological advancements (C90) | practical applications in forecasting (C53) |