Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1153977 | Statistics & Probability Letters | 2009 | 8 Pages |
Abstract
Many time series encountered in real applications display seasonal behavior. In this paper, we consider multiplicative seasonal vectorial autoregressive moving average (SVARMA) models to describe seasonal vector time series. We discuss conditional maximum likelihood estimation of the model parameters, allowing them to satisfy general linear constraints. Having fitted a model, residual autocovariances (or autocorrelations) have been found useful in checking time series models. Consequently, we obtain the asymptotic distributions of the residual autocovariance matrices. As applications of these results, Portmanteau test statistics are proposed and their asymptotic distributions are studied. The finite-sample properties of the test statistics are evaluated using Monte Carlo experiments.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Eugen Ursu, Pierre Duchesne,