  This article develops the asymptotic distribution of the least squares
estimator of the model parameters in periodicvector autoregressive time series
models (hereafter PVAR) with uncorrelated but dependent innovations. When
theinnovations are dependent, this asymptotic distributions can be quite
different from that of PVAR models with in-dependent and identically
distributed (iid for short) innovations developed in Ursu and Duchesne (2009).
Modifiedversions of the Wald tests are proposed for testing linear restrictions
on the parameters. These asymptotic results are illustrated by Monte Carlo
experiments. An application to a bivariate real financial data is also proposed
