Journal ArticleDOI
An introduction to long‐memory time series models and fractional differencing
TLDR
Generation and estimation of these models are considered and applications on generated and real data presented, showing potentially useful long-memory forecasting properties.Abstract:
. The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1-B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown to possibly arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.read more
Citations
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Journal ArticleDOI
On the self-similar nature of Ethernet traffic (extended version)
TL;DR: It is demonstrated that Ethernet LAN traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal-like behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks.
Journal ArticleDOI
A long memory property of stock market returns and a new model
TL;DR: In this paper, a Monte-Carlo analysis of stock market returns was conducted and it was found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return also has quite high autocorrelation for long lags.
Journal ArticleDOI
The estimation and application of long memory time series models
John Geweke,Susan Porter-Hudak +1 more
TL;DR: In this article, a new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic regressor.
Book
Wavelet Methods for Time Series Analysis
TL;DR: Wavelet analysis of finite energy signals and random variables and stochastic processes, analysis and synthesis of long memory processes, and the wavelet variance.
Journal ArticleDOI
Some properties of time series data and their use in econometric model specification
TL;DR: In this paper, it was suggested that some aspects of this practice should be brought out into the open, and the type of equations to be considered are generating equations, so that a simulation of the explanatory side should produce the major properties of the variable being explained.
References
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Journal ArticleDOI
Fractional Brownian Motions, Fractional Noises and Applications
Journal ArticleDOI
Spurious regressions in econometrics
Clive W. J. Granger,P. Newbold +1 more
TL;DR: In this paper, it is pointed out that it is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic.
Journal ArticleDOI
Long memory relationships and the aggregation of dynamic models
TL;DR: In this paper, it was shown that the aggregate series may have univariate long-memory models and obey integrated, or infinite length transfer function relationships, and that if series obeying such models occur in practice, from aggregation, then present techniques being used for analysis are not appropriate.
Journal ArticleDOI
Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon
A. I. McLeod,Keith W. Hipel +1 more
TL;DR: In this paper, the Akaike information criterion (AIC) is suggested as a method for choosing between a discrete fractional Gaussian noise (FGN) process and a Box-Jenkins model.
Journal ArticleDOI
A Fast Fractional Gaussian Noise Generator
TL;DR: The definition of fast fractional Gaussian noises, as sums of Markov-Gauss and other simple processes, fits the intuitive ideas that climate can be visualized as either unpredictably inhomogeneous or ruled by a hierarchy of variable regimes.