Results 171 to 180 of about 4,901 (213)

Analysing inflation by the fractionally integrated ARFIMA-GARCH model

Journal of Applied Econometrics, 1996
This paper considers the application of long-memory processes to describing inflation for 10 countries. We implement a new procedure to obtain approximate maximum likelihood estimates of an ARFIMA-GARCH process; which is fractionally integrated I(d) with a superimposed stationary ARMA component in its conditional mean.
Baillie, Richard T, Chung, Ching-Fan, Tieslau, Margie A   +2 more
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Optimal prediction with nonstationary ARFIMA model

Journal of Forecasting, 2007
AbstractWe 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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Preliminary estimation of ARFIMA models

2000
In 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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Long-Range Dependence and ARFIMA Models

2013
In 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, M. Levent Kavvas, Rovshan K. Abbasov   +2 more
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Modelling long-term heart rate variability: an ARFIMA approach

Biomedizinische Technik/Biomedical Engineering, 2006
Long-term heart rate variability (HRV) series can be described by time-variant autoregressive modelling. HRV recordings show dependence between distant observations that is not negligible, suggesting the existence of long-range correlations. In this work, selective adaptive segmentation combined with fractionally integrated autoregressive moving ...
Argentina S, Leite, Ana Paula, Rocha, M Eduarda, Silva, Ovídio, Costa   +3 more
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Network Anomaly Detection Based on ARFIMA Model

2015
In this paper, the estimation model ARFIMA is presented as a method of detecting anomalies in network traffic. Parameters estimation and model identification are performed with the use of algorithms of: Geweke and Porter-Hudak (estimation of the differencing parameters) and Box-Jankins (identification of the row of the model).
Tomasz Andrysiak, Łukasz Saganowski
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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
Olbermann, Barbara P., Lopes, Sílvia R. C., Reisen, Valdério A.   +2 more
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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.
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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.
Kwan, W, Li, WK, Li, G
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Modeling and predicting stock returns using the ARFIMA-FIGARCH

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models.
P. Bagavathi Sivakumar, V. P. Mohandas
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