Results 161 to 170 of about 2,441 (205)

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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An Evaluation of ARFIMA Programs

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, 2017
Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA ...
Kai Liu, Xi Zhang, YangQuan Chen
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BAYESIAN ANALYSIS OF VECTOR ARFIMA PROCESSES

Australian Journal of Statistics, 1997
Summary A general framework is presented for Bayesian inference of multivariate time series exhibiting long‐range dependence. The series are modelled using a vector autoregressive fractionally integrated moving‐average (VARFIMA) process, which can capture both short‐term correlation structure and long‐range dependence characteristics ...
Ravishanker, Nalini, Ray, Bonnie K.
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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
openaire   +4 more sources

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 ...
Craig Ellis
exaly   +2 more sources

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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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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Regularised Estimators for ARFIMA Processes

IFAC Proceedings Volumes, 2012
Abstract Stochastic processes with long-range dependence are found in many applications. ARFIMA models can be used to characterise both their short-term correlations and the phenomenon of long-range dependence. Maximum likelihood estimates of the model parameters have nice statistical properties but are ill-conditioned and hard to compute.
Oskar Vivero, William P. Heath
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Maximum likelihood estimation of the fractional differencing parameter in an ARFIMA model using wavelets

Mathematics and Computers in Simulation, 2002
In this paper, we examine the finite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional differencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coefficients.
Y K Tse, V V Anh
exaly   +2 more sources

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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