In this article we first revisit some earlier work on fractionally differenced white noise and correct some issues with previously published formulae. We then look at vector processes and derive formula for the Autocorrelation function, which is extended in this work to a larger range of parameter values than considered elsewhere, and compare this with previously published work
A vector time series model with long-memory dependence is introduced. It is assumed that, at each ti...
The classical autocorrelation function may not be an effective and informative means in revealing th...
Aspects of model building using fractionally differenced autoregressive-moving average processes are...
In this article we first revisit some earlier work on fractionally differenced white noise and corre...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
Abstract. In this paper we investigate the properties of the estimator of degree of differencing the...
We prove a representation of the partial autocorrelation function α(・) of a stationary process { Xn ...
Both the simulated white-noise (top left panel) and pink-noise (bottom left panel) time series conta...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
This paper derives the autocorrelation function of the squared values of long‐memory GARCH processes...
In this paper, we consider a method (splitting) for calculating the autocovariances of fractional in...
The Autocorrelation Function (ACF) was originally studied as a tool for analyzing dependence for Gau...
Previous work on log-periodogram regression in time series with long range dependence is reviewed. T...
A commonly used defining property of long memory time series is the power law decay of the autocovari...
A vector time series model with long-memory dependence is introduced. It is assumed that, at each ti...
The classical autocorrelation function may not be an effective and informative means in revealing th...
Aspects of model building using fractionally differenced autoregressive-moving average processes are...
In this article we first revisit some earlier work on fractionally differenced white noise and corre...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
Abstract. In this paper we investigate the properties of the estimator of degree of differencing the...
We prove a representation of the partial autocorrelation function α(・) of a stationary process { Xn ...
Both the simulated white-noise (top left panel) and pink-noise (bottom left panel) time series conta...
For an autoregressive fractionally integrated moving-average ARFIMA(p, d, q) process, it is often a ...
This paper derives the autocorrelation function of the squared values of long‐memory GARCH processes...
In this paper, we consider a method (splitting) for calculating the autocovariances of fractional in...
The Autocorrelation Function (ACF) was originally studied as a tool for analyzing dependence for Gau...
Previous work on log-periodogram regression in time series with long range dependence is reviewed. T...
A commonly used defining property of long memory time series is the power law decay of the autocovari...
A vector time series model with long-memory dependence is introduced. It is assumed that, at each ti...
The classical autocorrelation function may not be an effective and informative means in revealing th...
Aspects of model building using fractionally differenced autoregressive-moving average processes are...
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