Scinovex
articleTop 1% cited

Fractional differencing

Biometrika · 1981 · Vol. 68(1) · pp. 165–176
J. R. M. Hosking

Abstract

The family of autoregressive integrated moving-average processes, widely used in time series analysis, is generalized by permitting the degree of differencing to take fractional values. The fractional differencing operator is defined as an infinite binomial series expansion in powers of the backward-shift operator. Fractionally differenced processes exhibit long-term persistence and antipersistence; the dependence between observations a long time span apart decays much more slowly with time span than is the case with the more commonly studied time series models. Long-term persistent processes have applications in economics and hydrology; compared to existing models of long-term persistence, the family of models introduced here offers much greater flexibility in the simultaneous modelling of the short-term and long-term behaviour of a time series.

Complex Systems and Time Series AnalysisNeural Networks and ApplicationsMathematicsTerm (time)Series (stratigraphy)Autoregressive modelOperator (biology)Binomial (polynomial)Applied mathematicsEconometricsNegative binomial distributionStatistics
Citations
2,462
FWCI
18.43
field-weighted impact
References
0
Percentile
99%
vs. same field & year
Citations per year
Cited by
Long memory processes and fractional integration in econometrics
Journal of Econometrics · 1996 · 1,890 citations
Modeling and pricing long memory in stock market volatility
Journal of Econometrics · 1996 · 1,243 citations
Long-range dependence in variable-bit-rate video traffic
IEEE Transactions on Communications · 1995 · 1,161 citations
ARCH models as diffusion approximations
Journal of Econometrics · 1990 · 957 citations
DCCA cross-correlation coefficient: Quantifying level of cross-correlation
Physica A Statistical Mechanics and its Applications · 2010 · 602 citations
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.