Share This Article:

PC-VAR Estimation of Vector Autoregressive Models

Full-Text HTML Download Download as PDF (Size:301KB) PP. 251-259
DOI: 10.4236/ojs.2012.23030    5,273 Downloads   8,228 Views Citations
Author(s)

ABSTRACT

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.

Cite this paper

C. Morana, "PC-VAR Estimation of Vector Autoregressive Models," Open Journal of Statistics, Vol. 2 No. 3, 2012, pp. 251-259. doi: 10.4236/ojs.2012.23030.

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.