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Author: Donatas Surgailis Publisher: World Scientific Publishing Company ISBN: 1911299387 Category : Mathematics Languages : en Pages : 594
Book Description
Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980) and Hosking (1981) found examples of time series whose fractional difference becomes a short memory process, in particular, a white noise, while the initial series has unbounded spectral density at the origin, i.e. exhibits long memory.Further examples of data following long memory were found in hydrology and in network traffic data while in finance the phenomenon of strong dependence was established by dramatic empirical success of long memory processes in modeling the volatility of the asset prices and power transforms of stock market returns.At present there is a need for a text from where an interested reader can methodically learn about some basic asymptotic theory and techniques found useful in the analysis of statistical inference procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate level summarizing theoretical developments both for short and long memory processes and their applications to statistics. The book also contains some real data applications and mentions some unsolved inference problems for interested researchers in the field./a
Author: Donatas Surgailis Publisher: World Scientific Publishing Company ISBN: 1911299387 Category : Mathematics Languages : en Pages : 594
Book Description
Box and Jenkins (1970) made the idea of obtaining a stationary time series by differencing the given, possibly nonstationary, time series popular. Numerous time series in economics are found to have this property. Subsequently, Granger and Joyeux (1980) and Hosking (1981) found examples of time series whose fractional difference becomes a short memory process, in particular, a white noise, while the initial series has unbounded spectral density at the origin, i.e. exhibits long memory.Further examples of data following long memory were found in hydrology and in network traffic data while in finance the phenomenon of strong dependence was established by dramatic empirical success of long memory processes in modeling the volatility of the asset prices and power transforms of stock market returns.At present there is a need for a text from where an interested reader can methodically learn about some basic asymptotic theory and techniques found useful in the analysis of statistical inference procedures for long memory processes. This text makes an attempt in this direction. The authors provide in a concise style a text at the graduate level summarizing theoretical developments both for short and long memory processes and their applications to statistics. The book also contains some real data applications and mentions some unsolved inference problems for interested researchers in the field./a
Author: Liudas Giraitis Publisher: ISBN: 9781848162785 Category : Mathematics Languages : en Pages : 577
Book Description
A discrete-time stationary stochastic process with finite variance is said to have long memory if its autocorrelations tend to zero hyperbolically in the lag, i.e. like a power of the lag, as the lag tends to infinity. The absolute sum of autocorrelations of such processes diverges and their spectral density at the origin is unbounded. This is unlike the so-called weakly dependent processes, where autocorrelations tend to zero exponentially fast and the spectral density is bounded at the origin. In a long memory process, the dependence between the current observation and the one at a distant future is persistent; whereas in the weakly dependent processes, these observations are approximately independent. This fact alone is enough to warn a person about the validity of the classical inference procedures based on the square root of the sample size standardization when data are generated by a long-term memory process.The aim of this volume is to provide a text at the graduate level from which one can learn, in a concise fashion, some basic theory and techniques of proving limit theorems for numerous statistics based on long memory processes. It also provides a guide to researchers about some of the inference problems under long memory.
Author: Jan Beran Publisher: Springer Science & Business Media ISBN: 3642355129 Category : Mathematics Languages : en Pages : 892
Book Description
Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.
Author: Alex Gonzaga Publisher: ISBN: Category : Languages : en Pages :
Book Description
Local Lyapunov exponent (LLE) is a finite-time version of Lyapunov exponent, a tool for analyzing chaos. In this paper, we propose a new approach in analyzing long-memory time series. We apply LLE in the context of long-memory processes. The distribution function of the LLE for ARFIMA(p,d,q) process is derived, and an unbiased estimator and some uniformly most powerful tests for long-memory are proposed.
Author: Alex Gonzaga Publisher: ISBN: Category : Languages : en Pages :
Book Description
A long-memory process may be characterized by its corresponding wavelet variance, an analogue of the spectrum, which decomposes the variance of a process with respect to a variable called scale. In this paper, we derive the variance of the logarithm of the maximal-overlap estimator - a relatively efficient estimator of the wavelet variance. We use this to obtain a weighted-least-square estimator and a test for the long-memory parameter. We show that this weighted-least-square estimator is more statistically efficient than the one based on the wavelet-transform estimator of the wavelet variance. Finally, we apply these estimators and tests to determine the long-memory parameter of the Nile river data, a well-known long-memory process.
Author: Donatas Surgailis
Author: Donatas Surgailis
Author: Liudas Giraitis
Author: Liudas Giraitis
Author: Hongwen Guo
Author: Jan Beran
Author: Philip Preuß
Author: Alex Gonzaga
Author: Alex Gonzaga
Author: Josu Arteche
Author: Timothy Graves