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An adaptive broadband estimator of the fractional differencing coefficient

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
We consider semiparametric fractional exponential (FEXP) estimators of the memory parameter d for a potentially nonstationary linear long-memory time series with smooth additive trend. We use differencing to annihilate the trend, followed by tapering to handle the potential non-invertibility of the differenced series. We propose a method of pooling the
C. Hurvich, E. Moulines, P. Soulier
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Empirical study of ARFIMA model based on fractional differencing

Physica A: Statistical Mechanics and its Applications, 2007
Abstract In this paper, we studied the long-term memory of Hong Kong Hang Sheng index using MRS analysis, established ARFIMA model for it, and detailed the procedure of fractional differencing. Furthermore, we compared the ARFIMA model built by this means with the one that took first-order differencing as an alternative.
Jin Xiu, Yao Jin
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On fractionally differenced periodic processes

2010
Summary: Long memory time series have been a topic of considerable recent interest. Applications of such processes have been made to hydrology, meteorology and economics. This paper considers modelling periodic processes with long term dependence patterns existing in the data.
Hui, YV, Li, WK
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Forecasting costs incurred from unit differencing fractionally integrated processes

International Journal of Forecasting, 1994
This paper investigates the cost of the assuming a unit difference when the series is only fractionally integrated with an integration parameter d not-equal 1. Studies have pointed to the low power of unit root tests against a fractionally integrated alternative, and have noted the performance of these tests is worse than against nearly integrated ...
Jeremy Smith, Sanjay Yadav
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Generalized exponential time differencing for fractional oscillation models

Journal of Computational and Applied Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aljowhara H. Honain   +3 more
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FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY1

JAWRA Journal of the American Water Resources Association, 1985
ABSTRACT: Fractional differencing is a tool for modeling time series which have long‐term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box‐Jenkins ARIMA(p, d, q) models to take ...
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A wavelet solution to the spurious regression of fractionally differenced processes

Applied Stochastic Models in Business and Industry, 2003
AbstractIn this paper we propose to overcome the problem of spurious regression between fractionally differenced processes by applying the discrete wavelet transform (DWT) to both processes and then estimating the regression in the wavelet domain. The DWT is known to approximately decorrelate heavily autocorrelated processes and, unlike applying a ...
Fan, Yanqin, Whitcher, Brandon
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Identification of fractional differencing autoregressive models

Communications in Statistics - Theory and Methods, 1995
This paper proposes an identification method to fractional differencing autoregressive models, and this method gives a consistent estimator for fractional differencing order and efficient estimates for parameters in fractional differencing autoregressive models.
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Interest parity, fractional differencing, and the strength of attraction

Global Finance Journal, 1999
Abstract This article discusses the strength of attraction in the cointegration of foreign exchange futures prices and their own cash prices under a cost-of-carry futures pricing model. The memories of the residuals in cointegration regression are analyzed by using the fractional cointegration of Cheung and Lai (1993) and the data-tapered method ...
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Spectral approximation to the fractional differencing operator

2006
The article proposes a criterion for the estimation of the best ARMA(1,1) process which approximates a given fractionally integrated process.
PICCOLO, DOMENICO, CORDUAS, MARCELLA
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