Macroeconomic forecasting and structural analysis through regularized reduced-rank regression
This paper proposes a strategy to detect and impose reduced-rank restrictions in medium vector autoregressive models. In this framework, it is known that Canonical Correlation Analysis (CCA) does not perform well because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naive univariate forecasting methods. In particular, we suggest to use a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, thus obtaining a method that is asymptotically equivalent to CCA, but it is numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach both in forecasting and in structural analysis.
|Date of creation:||03 Oct 2013|
|Date of revision:||03 Oct 2013|
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- Sancetta, A., 2006.
"Sample Covariance Shrinkage for High Dimensional Dependent Data,"
Cambridge Working Papers in Economics
0637, Faculty of Economics, University of Cambridge.
- Sancetta, Alessio, 2008. "Sample covariance shrinkage for high dimensional dependent data," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 949-967, May.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011.
"Forecasting large datasets with Bayesian reduced rank multivariate models,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, 08.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute.
- Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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