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.

Bernardini, E., Cubadda, G. (2014). Macroeconomic forecasting and structural analysis through regularized reduced-rank regression. INTERNATIONAL JOURNAL OF FORECASTING [10.1016/j.ijforecast.2013.10.005].

Macroeconomic forecasting and structural analysis through regularized reduced-rank regression

CUBADDA, GIANLUCA
2014-02-01

Abstract

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.
1-feb-2014
Online ahead of print
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-S/03 - STATISTICA ECONOMICA
English
Con Impact Factor ISI
canonical correlation analysis; vector autoregressive models; shrinkage estimation; macroeconomic prediction
Pubblicato a stampa: Volume 31, Issue 3, July–September 2015, Pages 682–691
Bernardini, E., Cubadda, G. (2014). Macroeconomic forecasting and structural analysis through regularized reduced-rank regression. INTERNATIONAL JOURNAL OF FORECASTING [10.1016/j.ijforecast.2013.10.005].
Bernardini, E; Cubadda, G
Articolo su rivista
File in questo prodotto:
File Dimensione Formato  
Bernardini&Cubadda_IJoF.pdf

accesso aperto

Descrizione: articolo completo
Dimensione 358.02 kB
Formato Adobe PDF
358.02 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/91089
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 16
social impact