An Introduction to Long-Memory Time Series Models and Fractional Differencing

2008 ◽  
pp. 321-337 ◽  
Author(s):  
C. W. J. Granger
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Models ◽  
Fractional Differencing ◽  
Memory Time

On the estimation of short memory components in long memory time series models

2016 ◽  
Vol 20 (4) ◽  
Author(s):  
Richard T. Baillie ◽  
George Kapetanios
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Models ◽  
Memory Process ◽  
Long Memory Process ◽  
Fractional Differencing ◽  
Memory Time ◽  
Short Memory ◽  
Short Memory Process ◽  
Memory Parameter

AbstractA substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory

scholarly journals Confidence Regions for Parameters in Stationary Time Series Models With Gaussian Noise

2022 ◽  
Vol 9 ◽  
Author(s):  
Xiuzhen Zhang ◽  
Riquan Zhang ◽  
Zhiping Lu
Keyword(s):  
Time Series ◽  
Empirical Likelihood ◽  
Long Memory ◽  
Score Function ◽  
Real Data ◽  
Confidence Regions ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Finite Sample ◽  
Memory Time

This article develops two new empirical likelihood methods for long-memory time series models based on adjusted empirical likelihood and mean empirical likelihood. By application of Whittle likelihood, one obtains a score function that can be viewed as the estimating equation of the parameters of the long-memory time series model. An empirical likelihood ratio is obtained which is shown to be asymptotically chi-square distributed. It can be used to construct confidence regions. By adding pseudo samples, we simultaneously eliminate the non-definition of the original empirical likelihood and enhance the coverage probability. Finite sample properties of the empirical likelihood confidence regions are explored through Monte Carlo simulation, and some real data applications are carried out.

A class of nearly long-memory time series models

2002 ◽  
Vol 18 (2) ◽  
pp. 265-281 ◽  
Author(s):  
F.Jay Breidt ◽  
Nan-Jung Hsu
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Models ◽  
Memory Time
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