PC-VAR Estimation of Vector Autoregressive Models
Claudio Morana
Università di Milano Bicocca; International Centre for Economic Research (ICER); Center for Economic Research on Pensions and Welfare Policies (CeRP); Fondazione Eni Enrico Mattei (FEEM)
February 11, 2012
Open Journal of Statistics, 2012, 2, 251-259
Abstract:
In this paper PC-VAR estimation of vector autoregressive models (VAR) is proposed. The estimation strategy successfully lessen the curse of dimensionality affecting VAR models, when estimated using sample sizes typically available in quarterly studies. The procedure involves a dynamic regression using a subset of principal components extracted from a vector time series, and the recovery of the implied unrestricted VAR parameter estimates by solving a set of linear constraints. PC-VAR and OLS estimation of unrestricted VAR models show the same asymptotic properties. Monte Carlo results strongly support PC-VAR estimation, yielding gains, in terms of both lower bias and higher efficiency, relatively to OLS estimation of high dimensional unrestricted VAR models in small samples. Guidance for the selection of the number of components to be used in empirical studies is provided.
Keywords: vector autoregressive model, principal components analysis, statistical reduction techniques
JEL Classification: C22
Accepted Paper Series
Suggested Citation
Morana, Claudio, PC-VAR Estimation of Vector Autoregressive Models (February 11, 2012). Open Journal of Statistics, 2012, 2, 251-259. Available at SSRN: http://ssrn.com/abstract=2006847 or http://dx.doi.org/10.2139/ssrn.2006847
