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Bayesian prediction for vector ARFIMA processes
International Journal of Forecasting, 2002Abstract We provide explicit formulae for the joint predictive distribution of a Gaussian vector autoregressive fractionally integrated moving average (VARFIMA) process and describe a Bayesian method for its feasible evaluation. Inference for the parameters in the Bayesian framework is based on the joint posterior distribution of the model parameters
Nalini Ravishanker, Bonnie K. Ray
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Arfima-Figarch vs. Arfima-Hygarch: Case Study ETF Returns of Emerging Asian Countries
Asian Journal of Finance & Accounting, 2014This research investigate the long memory returns for ETF returns index of seven Asian countries in Emerging Markets Equities during 2008-2013 periods. Those ETFs are Wisdom Tree Indian Rupee Fund (ICN), Market Vectors Indonesia Index (IDX), iShares MSCI Malaysia Index Fund (EWM), Market Vectors Russia ETF (RSX), and iShares MSCI Thailand Investable ...
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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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Indirect Inference for ARFIMA Processes
IFAC Proceedings Volumes, 1998Abstract Inference for ARFIMA processes is more complicated than that for ordinary ARMA models. This has limited the application of these processes in the practice of modelling economic time series. In this paper, a procedure of indirect estimation for fractionally integrated processes is proposed and discussed.
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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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Preliminary estimation of ARFIMA models
2000In this article we propose a preliminary estimator for the parameters of an ARFIMA(p,d,q) model. The estimation procedure is based on the search of the element in the class of ARFIMA models closest to the estimated ARMA model which best fits the observed time series.
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A permanent-transitory decomposition for ARFIMA processes
Journal of Statistical Planning and Inference, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ariño, Miguel A., Marmol, Francesc
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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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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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Long-Range Dependence and ARFIMA Models
2013In this chapter, long-range dependence concept, Hurst phenomenon and ARFIMA models are introduced and the earlier work on these subjects are reviewed. Several methodologies are introduced for the estimation of long-range dependence index (Hurst number or fractional difference parameter).
Ali Ercan +2 more
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