We propose a method to identify common persistent components in a k-dimensional time series. Assuming that the individual series of the vector process have long-range dependence, we apply canonical correlation analysis to the series and its lagged values. A zero canonical correlation implies the existence of a short-memory linear combination, hence the existence of common long-range dependence;...

The long range dependence paradigm appears to be a suitable description of the data generating process for many observed economic time series. This is mainly due to the fact that it naturally characterizes time series displaying a high degree of persistence, in the form of a long lasting e ect of unanticipated shocks, yet exhibiting mean reversion. Whereas linear long range dependent time serie...

The aim of this paper is to surround the volatility dynamics on the Tunisian stock market via an approach founded on the detection of persistence phenomenon and longterm memory presence. More specifically, our object is to test whether long-term dependent processes are appropriated for modelling Tunisian stock market volatility. The empirical investigation has been driven on the two Tunisian st...

This paper proposes a model specification testing procedure for parametric specification of the conditional mean function in a nonlinear time series model with long–range dependence. An asymptotically normal test is established even when long–range dependence is involved. In order to implement the proposed test in practice using a simulated example, a bootstrap simulation procedure is establish...

Wireless applications, protocols and simulators can significantly benefit from thorough understanding and accurate modeling of the medium access control (MAC) layer residual bit-errors at high bitrates. In this paper, we analyze and model the biterrors encountered at the highest achievable 11 Mbps data rate of an 802.11b wireless local area network. We employ autocorrelation, aggregation and va...

| An on-line version of the Abry-Veitch wavelet based estimator of the Hurst parameter is presented. It has very low memory and computational requirements and scales naturally to arbitrarily high data rates, enabling its use in real-time applications such as admission control, and avoiding the need to store huge data sets for oo-line analysis. An implementation for Ethernet based on standard ha...

We present the results of a simulation study into the properties of 12 different estimators of the Hurst parameter, H, or the fractional integration parameter, d, in long memory time series. We compare and contrast their performance on simulated Fractional Gaussian Noises and fractionally integrated series with lengths between 100 and 10,000 data points and H values between 0.55 and 0.90 or d v...

This paper introduces a model to study the phenomenon of long range dependence. This model consists of an infinite superposition of independent Markovian ON/OFF– sources. A condition for assuring long range dependence is given and the Hurst parameter together with the correlation decay is derived for a specific example. We also give a physical interpretation of the existing long range dependenc...

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Künsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to circumvent these problems. Instead of approximating the short-run component of the spectrum, φ(λ), by a cons...

| There is much experimental evidence that network traac processes exhibit ubiquitous properties of self-similarity and long range dependence (LRD), i.e., of correlations over a wide range of time scales. However, there is still considerable debate about how to model such processes and about their impact on network and application performance. In this paper, we argue that much recent modeling w...