Groupe d’études et de recherche en analyse des décisions

G-2008-64

On Modeling and Diagnostic Checking of Vector Periodic Autoregressive Time Series Models

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Vector periodic autoregressive time series models (PVAR) form an important class of time series for modeling data derived from climatology, hydrology, economics, and electrical engineering, amongst others. In this article, we derive the asymptotic distributions of the least squares estimators of the model parameters in PVAR models, allowing the parameters in a given season to satisfy linear constraints. Residual autocorrelations from classical vector autoregressive and moving average models have been found useful for checking the adequacy of a particular model. In view of this, we obtain the asymptotic distribution of the residual autocovariance matrices in the class of PVAR models, and the asymptotic distribution of the residual autocorrelation matrices is given as a corollary. Portmanteau test statistics designed for diagnosing the adequacy of PVAR models are introduced and we study their asymptotic distributions. The proposed test statistics are illustrated in a small simulation study, and an application with bivariate quarterly West German data is presented.

, 29 pages