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J.R.G. Lansang, E.B. Barrios, “Principal Components Analyisis of Nonstationary Time Series Data”, Statistics and Computing, Vol. 19, No. 2, 2009, pp. 173-187. doi: 10.1007/s11222-008-9082-y

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.