Results 131 to 140 of about 467 (180)
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Jeffrey's divergence between ARFIMA processes
Digital Signal Processing, 2018Abstract The symmetric Kullback–Leibler divergence known as Jeffrey's divergence (JD) has found applications in signal and image processing, from radar clutter modeling to texture analysis. Recently, several studies were done on the JD between ergodic wide-sense stationary autoregressive (AR) and/or moving average (MA) processes.
Mahdi Saleh +2 more
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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
Barbara P. Olbermann +2 more
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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
Erol Egrioglu, Süleyman Günay
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BAYESIAN ANALYSIS OF VECTOR ARFIMA PROCESSES
Australian Journal of Statistics, 1997Summary 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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Forecasting realised volatility using ARFIMA and HAR models [PDF]
Quantitative Finance, 2019 Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010.
M Kabir Hassan +2 more
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On the estimation and diagnostic checking of the ARFIMA–HYGARCH model
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wilson Kwan, Wai Keung Li, Guodong Li
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Regularised Estimators for ARFIMA Processes
IFAC Proceedings Volumes, 2012Abstract 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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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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Estimation and testing of ARFIMA models in the real exchange rate
International Journal of Finance & Economics, 2002AbstractThe Purchasing Power Parity (PPP) hypothesis is one of the most important theoretical relationships in international economics. However, its empirical support remains controversial. We propose an alternative way of modelling the real exchange rate in five industrialized countries in relation to the US dollar, by means of fractionally integrated
GIL-ALANA, Luis A., TORO, Juan
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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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