Macroeconomic forecasting and structural analysis through regularized reduced-rank regression

This paper proposes a strategy for detecting and imposing reduced-rank restrictions in medium vector autoregressive models. It is known that Canonical Correlation Analysis (CCA) does not perform well in this framework, because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naïve univariate forecasting methods. In particular, we suggest the usage of a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, in order to obtain a method that is asymptotically equivalent to CCA, but numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach for both forecasting and structural analysis.