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Asymptotic Properties of QML Estimators for VARMA Models with Time‐dependent Coefficients
Author(s) -
Alj Abdelkamel,
Azrak Rajae,
Ley Christophe,
Mélard Guy
Publication year - 2017
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12268
Subject(s) - mathematics , estimator , heteroscedasticity , series (stratigraphy) , bivariate analysis , asymptotic analysis , univariate , autoregressive–moving average model , autoregressive model , monte carlo method , asymptotic distribution , gaussian , statistics , multivariate statistics , paleontology , physics , quantum mechanics , biology
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.