Open AccessPC-VAR Estimation of Vector Autoregressive Models
Author(s) -
Claudio Morana
Publication year - 2012
Publication title -
open journal of statistics
Language(s) - English
Resource type - Journals
eISSN - 2161-7198
pISSN - 2161-718X
DOI - 10.4236/ojs.2012.23030
Subject(s) - autoregressive model , vector autoregression , curse of dimensionality , mathematics , monte carlo method , econometrics , model selection , series (stratigraphy) , ordinary least squares , estimation , principal component analysis , statistics , economics , management , paleontology , biology
In this paper PC-VAR estimation of vector autoregressive models (VAR) is proposed. The estimation strategy successfully lessens 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