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Empirical study of ARFIMA model based on fractional differencing
Physica A: Statistical Mechanics and its Applications, 2007Abstract 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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Another look at the forecast performance of ARFIMA models
International Review of Financial Analysis, 2004This paper investigates the out-of-sample forecast performance of the autoregressive fractionally integrated moving average [ARFIMA (0,d,0)] specification, both when the underlying value of the fractional differencing parameter (d) is known a priori and when it is unknown.
Ellis, Craig, Wilson, Patrick J.
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Bayesian Inference for ARFIMA Models
Journal of Time Series Analysis, 2019This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations.
Durham, Garland +3 more
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Adaptive ARFIMA models with applications to inflation
Economic Modelling, 2012Abstract Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying intercept.
Morana, C, Baillie, RT
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Bayesian model selection in ARFIMA models
Expert Systems with Applications, 2010Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both
Eǧrïoǧlu, Erol, Günay, Süleyman
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Modeling of PMU Data Using ARFIMA Models
2018 Clemson University Power Systems Conference (PSC), 2018Installing Phasor Measurement Units (PMUs) in the smart grid has played an important role in having more reliable and secure grid. Due to the high sampling rate (50 samples/s), PMU generates massive amount of data compared to the conventional SCADA system.
Laith Shalalfeh +2 more
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Parametric estimation for ARFIMA models via spectral methods
Statistical Methods & Applications, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
COLI, Mauro +2 more
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Indirect estimation of ARFIMA and VARFIMA models
Journal of Econometrics, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin, Vance L., Wilkins, Nigel P.
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Bayesian analysis of long memory and persistence using ARFIMA models [PDF]
Journal of Econometrics, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
KOOP , Gary +3 more
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Optimal prediction with nonstationary ARFIMA model
Journal of Forecasting, 2007AbstractWe propose two methods to predict nonstationary long‐memory time series. In the first one we estimate the long‐range dependent parameterdby using tapered data; we then take the nonstationary fractional filter to obtain stationary and short‐memory time series.
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