Temporal aggregation in a periodically integrated autoregressive process
نویسندگان
چکیده
منابع مشابه
On the existence of Hilbert valued periodically correlated autoregressive processes
In this paper we provide sufficient condition for existence of a unique Hilbert valued ($mathbb{H}$-valued) periodically correlated solution to the first order autoregressive model $X_{n}=rho _{n}X_{n-1}+Z_{n}$, for $nin mathbb{Z}$, and formulate the existing solution and its autocovariance operator. Also we specially investigate equivalent condition for the coordinate process...
متن کاملAggregation of isotropic autoregressive fields
This note constitutes a corrigendum to the article of Azomahou (2009), JSPI, 139:25812597. The aggregation of isotropic four nearest neighbors autoregressive models on the lattice Z , with random coefficient, is investigated. The spectral density of the resulting random field is studied in details for a large class of law of the AR coefficient. Depending on this law, the aggregated field may ex...
متن کاملTemporal Aggregation of a Strong PGARCH(1,1) Process
Bollerslev’s (1986) standard GARCH(1,1) model has been successful in the literature of volatility modelling and forecasting in the past two decades. Many of its extensions are contributed to examine the stylized features often observed with financial asset data. One of the distinct success is Bollerslev and Ghysels’ (1996) periodic GARCH model, which takes into account periodic variation in the...
متن کاملTwo-stage Procedure in P-Order Autoregressive Process
In this paper, the two-stage procedure is considered for autoregressive parameters estimation in the p-order autoregressive model ( AR(p)). The point estimation and fixed-size confidence ellipsoids construction are investigated which are based on least-squares estimators. Performance criteria are shown including asymptotically risk efficient, asymptotically efficient, and asymptotically consist...
متن کاملSpatial-Temporal Autoregressive Dynamic Model
Although a myriad of methods have been advanced to tackle spatial and temporal structures in data separately, it becomes difficult to analyze these data using classical linear regression models when spatial-temporal structures coexist, especially when the data size is relatively large. In this article, we demonstrate a simple to implement method to handle spatial-temporal structures simultaneou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics & Probability Letters
سال: 1996
ISSN: 0167-7152
DOI: 10.1016/0167-7152(95)00225-1