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Bayesian Analysis of Long Memory and Persistence using ARFIMA Models [PDF]
Journal of Econometrics, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
G. Koop, E. Ley, J. Osiewalski, M. Steel
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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).
A. Ercan, M. Kavvas, R. Abbasov
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Invariance of the first difference in ARFIMA models
Computational Statistics, 2006The 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
B. P. Olbermann, S. Lopes, V. Reisen
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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.
Craig Ellis, P. Wilson
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Adjusted jackknife empirical likelihood for stationary ARMA and ARFIMA models
, 2020In this paper, jackknife empirical likelihood is proposed to be applied in stationary time series models. By applying the jackknife method to Whittle estimator, we obtain new asymptotically independent pseudo samples which will be used to construct ...
Xiuzhen Zhang +3 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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FUDMA Journal of Sciences
Internally generated revenue (IGR) is an important source of revenue that can be used to fund public services and infrastructure projects. Accurate forecasting of IGR is essential for effective budgeting and financial planning.
M.I. Usman, T. Musa, A. Ibrahim
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Internally generated revenue (IGR) is an important source of revenue that can be used to fund public services and infrastructure projects. Accurate forecasting of IGR is essential for effective budgeting and financial planning.
M.I. Usman, T. Musa, A. Ibrahim
semanticscholar +1 more source
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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Analysing inflation by the fractionally integrated ARFIMA-GARCH model
Journal of Applied Econometrics, 1996This 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 +2 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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