A complete convergence theorem for stationary regularly varying multivariate time series
نویسندگان
چکیده
منابع مشابه
Regularly varying multivariate time series
Abstract: A multivariate, stationary time series is said to be jointly regularly varying if all its finite-dimensional distributions are multivariate regularly varying. This property is shown to be equivalent to weak convergence of the conditional distribution of the rescaled series given that, at a fixed time instant, its distance to the origin exceeds a threshold tending to infinity. The limi...
متن کاملTail Dependence for Regularly Varying Time Series
We use tail dependence functions to study tail dependence for regularly varying RV time series. First, tail dependence functions about RV time series are deduced through the intensity measure. Then, the relation between the tail dependence function and the intensity measure is established: they are biuniquely determined. Finally, we obtain the expressions of the tail dependence parameters based...
متن کاملA version of Fabry’s theorem for power series with regularly varying coefficients
For real power series whose non-zero coefficients satisfy |am| → 1, we prove a stronger version of Fabry’s theorem relating the frequency of sign changes in the coefficients and analytic continuation of the sum of the power series. AMS Subj. Class.: 30B10, 30B40. For a set Λ of non-negative integers, we consider the counting function n(x,Λ) = #Λ ∩ [0, x]. We say that Λ is measurable if the limit
متن کاملA Time Varying Multivariate Autoregressive Modeling of Econometric Time Series
This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views reflected in these reports are not necessarily those of the Census Bureau nor do they necessarily represent Census Bureau statistical policy or practice .
متن کاملA new adaptive exponential smoothing method for non-stationary time series with level shifts
Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...
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: Extremes
سال: 2016
ISSN: 1386-1999,1572-915X
DOI: 10.1007/s10687-016-0253-5