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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.
Eduardo Ley, Jacek Osiewalski, Mark F J Steel   +2 more
exaly   +5 more sources

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Another look at the forecast performance of ARFIMA models

International Review of Financial Analysis, 2004
This 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.
exaly   +3 more sources

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
exaly   +2 more sources

Bayesian Inference for ARFIMA Models

Journal of Time Series Analysis, 2019
This 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, Geweke, John, Porter-Hudak, Susan, Sowell, Fallaw   +3 more
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Bayesian model selection in ARFIMA models

Expert Systems with Applications, 2010
Various 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
Erol Egrioglu, Süleyman Günay
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Adaptive ARFIMA models with applications to inflation

Economic Modelling, 2012
Abstract 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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Invariance of the first difference in ARFIMA models

Computational Statistics, 2006
The main goal of the paper is to analyze which estimation method for the fractional parameter is invariant to first-differencing when the model is described by an ARFIMA(p,d,q) process. The authors consider the performance of four estimation methods, belonging to parametric and semiparametric classes, for non-stationary ARFIMA models with main interest
Barbara P. Olbermann, Sílvia R. C. Lopes, Valderio Anselmo Reisen   +2 more
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On the estimation and diagnostic checking of the ARFIMA–HYGARCH model

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wilson Kwan, Wai Keung Li, Guodong Li
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Modeling of PMU Data Using ARFIMA Models

2018 Clemson University Power Systems Conference (PSC), 2018
Installing 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, Paul Bogdan, Edmond Jonckheere   +2 more
openaire   +1 more source