non-stationary process, multivariate time series, time-varying models; Statistics and Probability; Statistics, Probability and Uncertainty; non-stationary process; multivariate time series; time-varying models
Abstract :
[en] This paper is about vector autoregressive-moving average models with time-dependent coefficients to represent non-stationary time series. Contrary to other papers in the univariate case, the coefficients depend on time but not on the series' length n. Under appropriate assumptions, it is shown that a Gaussian quasi-maximum likelihood estimator is almost surely consistent and asymptotically normal. The theoretical results are illustrated by means of two examples of bivariate processes. It is shown that the assumptions underlying the theoretical results apply. In the second example, the innovations are marginally heteroscedastic with a correlation ranging from −0.8 to 0.8. In the two examples, the asymptotic information matrix is obtained in the Gaussian case. Finally, the finite-sample behaviour is checked via a Monte Carlo simulation study for n from 25 to 400. The results confirm the validity of the asymptotic properties even for short series and the asymptotic information matrix deduced from the theory.
Disciplines :
Mathematics Business & economic sciences: Multidisciplinary, general & others
Author, co-author :
Alj, Abdelkamel; Faculté des Sciences Juridiques, Économiques et Sociales, Université Moulay Ismail, Meknès, Morocco
Azrak, Rajae; Faculté des Sciences Juridiques, Économiques et Sociales, Université Mohammed V, Rabat, Morocco
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
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