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M.H. Pesaran, T. Schuerman, S.M. Weiner, “Modelling Regional Interdependencies Using a Global Error Correcting Macroeconometric Model”, Journal of Business and Economic Statistics, Vol. 22, No. 2, 2004, 129-162. doi: 10.1198/073500104000000019

has been cited by the following article:

  • TITLE: PC-VAR Estimation of Vector Autoregressive Models

    AUTHORS: Claudio Morana

    KEYWORDS: Vector Autoregressive Model; Principal Components Analysis; Statistical Reduction Techniques

    JOURNAL NAME: Open Journal of Statistics, Vol.2 No.3, July 6, 2012

    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.