Time Series with Long Memory

By Peter M. Robinson. 2003

291 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b102731d6787b9eaa902fd88509e597f9a8591c3

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Citations in the corpus

Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting (2006, background): “For excellent surveys of long memory processes and fractional models see Robinson (1994c), Baillie (1996), and Robinson (2003), and for a textbook treatment see e.g. Beran (1994).”
The Volatility of Realized Volatility (2005, background): “Historically, the first class of long-memory models has been the fractionally integrated process proposed by Granger and Joyeux (1980) and Hosking (1981) (for comprehensive surveys see Beran, 1994; Robinson, 2003 ).”
A simple test of changes in mean in the possible presence of long‐range dependence (2011, background): “In the last two decades, we have witnessed a rapid development for statistical inference of long-range dependent (or long memory) time series; see Beran (1994), Doukhan et al. (2003), Robinson (2003), Teyssière and Kirman (2005) among others for book-length treatments of this topic.”
Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices (2007, methodology): “Estimation methods and algorithms for periodic ARMA models have been developed by Pagano (1978) , Vecchia (1985), and Li and Hui (1988), among others.”
Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process (2011, methodology): “Earlier, Kolmogorov (1940), when studying turbulence, had proposed a mathematical model to describe this behaviour, which was further developed by Mandelbrot and van Ness (1968) and has been known as simple scaling stochastic model or fractional Gaussian noise (see Beran 1994; Embrechts and Maejima ”
A Practical Guide to Forecasting Financial Market Volatility (2005, background): “Books that cover specific aspects or variants of volatility models include Franses and van Dijk (2000) on nonlinear models, and Beran (1994) and Robinson (2003) on long memory models.”
Long-Range Dependent Curve Time Series (2020, background): “On the other hand, there have been many notable developments in the time series literature, especially over the past 30 years or so, on long-range dependent, or long memory, time series models (see, e.g., Beran 1994; Robinson 2003; Palma 2007; Giraitis, Koul, and Surgailis 2012; Beran et al. 2013).”
Estimators for Long Range Dependence: An Empirical Study (2009, background): “See the volumes by Beran (1994), Embrechts and Maejima (2002), and Palma (2007) and the collections of Doukhan et al. (2003) and Robinson (2003) and the references therein.”
Asymptotic theory of least squares estimators for nearly unstable processes under strong dependence (2007, background): “, Robinson [22, 24] and Doukhan, Oppenheimer and Taqqu [11]) which cannot be encompassed by the martingale difference setting of Chan and Wei [4].”
Long Range Dependence (2007, background): “The only exception is the case 0 < α < 1, H = 1/α, which corresponds to a single process, the SαS Lévy motion; see Samorodnitsky and Taqqu (1990). It is common to use the increments of certain SαS SSSI processes as canonical heavy tailed models with long range dependence.”
Modelling structural breaks, long memory and stock market volatility: an overview (2005, background): “Baillie (1996), Doukhan et al. (2003), and Robinson (1994a, 2003) have provided comprehensive surveys on long memory processes and fractional integration while a recent annals issue of this journal entitled Long Memory and Nonlinear Time Series, edited by Davidson and Terasvirta (2002), has dealt…”