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Large Sample Inference For Long Memory Processes by Liudas Giraitis

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.

Readership: Students and professionals in statistics, econometrics and finance.

Large Sample Inference For Long Memory Processes
Author:
by Liudas Giraitis
Title:
Large Sample Inference For Long Memory Processes
ISBN:
1848162782
ISBN13:
978-1848162785
Publisher:
Imperial College Press; 1 edition (April 30, 2012)
Language:
English
Category:
Mathematics
Size ePub version:
1574 kb
Size PDF version:
1743 kb
Rating:
3.8
Votes:
381
Pages:
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