Results 231 to 240 of about 11,080 (251)
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Fractional Differencing, Long Memory, and Predictive Structure
We propose a finite-sample estimator for the fractional differencing order đť‘‘ of equity log-price series, motivated by the empirical failure of classical frequency-domain estimators-including the extended Whittle estimator-to resolve economically meaningful cross-sectional heterogeneity in long-range dependence.openaire +1 more source
Estimating the Fractionally Integrated Model with a Break in the Differencing Parameter [PDF]
This note examines a new problem in the structural-change literature. A fractionally integrated model is assumed to experience a change in the differencing parameter at an unknown time. We develop consistent estimators of the change point and the pre- and post-shift differencing parameters.
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Autoregressive Fractionally Integrated Moving Average or Fractional Differencing
2021openaire +1 more source
Analysis of Fractionally Differenced Processes with Heteroscedastic Errors
2018The prime goal of this research is to model the long-range dependency and volatility factors fitting in fractionally differenced ARMA (ARFIMA) and Gegenbauer ARMA processes (GARMA) in financial time series. This extends the efficiency in computing the exact maximum likelihood established by Sowell through conditional quasi maximum likelihood (QMLE) for
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Advancement of Fractionally Differenced Gegenbauer Processes with Long Memory
2015The class of long memory time series models involving Gegenbauer processes is investigated in detail in terms of formulation, parameter estimation, prediction and testing. Corresponding truncated AR (autoregressive) and MA (moving average) approximations driven by Gaussian white noise are analysed through state space modelling and Kalman filtering to ...
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FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY
Journal of the American Water Resources Association, 1985J R M Hosking
exaly
No-cointegration test based on fractional differencing: Some Monte Carlo results
Journal of Statistical Planning and Inference, 1999Y K Tse, Võ Anh
exaly

