Results 181 to 190 of about 7,220 (228)
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Adaptive ARFIMA models with applications to inflation
Economic Modelling, 2012Abstract 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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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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An Evaluation of ARFIMA Programs
Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, 2017Strong 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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Comparing ARFIMA and ARIMA Models in Forecasting under Five Mortality Rate in Tanzania
Asian Journal of Probability and StatisticsTanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow.
Sadock Aron Mwijalilege +2 more
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arXiv.org
Accurate forecasting of exchange rates remains a persistent challenge, particularly for emerging economies such as Brazil, Russia, India, and China (BRIC).
Tanujit Chakraborty +3 more
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Accurate forecasting of exchange rates remains a persistent challenge, particularly for emerging economies such as Brazil, Russia, India, and China (BRIC).
Tanujit Chakraborty +3 more
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Asian Journal of Probability and Statistics
This study investigates the dynamics of commercial banks’ maximum lending rates in Nigeria using short-memory ARIMA and long-memory models such as ARFIMA and the FIGARCH models. The data for the study spanned from January 1997 to May 2024.
G. L. Tuaneh +2 more
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This study investigates the dynamics of commercial banks’ maximum lending rates in Nigeria using short-memory ARIMA and long-memory models such as ARFIMA and the FIGARCH models. The data for the study spanned from January 1997 to May 2024.
G. L. Tuaneh +2 more
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Inflation persistence for product groups in Brazil using the ARFIMA-GARCH model
Macroeconomics and Finance in Emerging Market Economies, 2022This study uses ARFIMA-GARCH models and structural break tests to analyse the persistence of Brazilian inflation. Its main contribution is to carry out a disaggregated analysis for nine product groups.
Adriana Ferreira Silva +2 more
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Research Review: Jurnal Ilmiah Multidisiplin
This study aims to compare the effectiveness of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model and the Autoregressive Integrated Moving Average–Feedforward Neural Network (ARIMA-FFNN) hybrid model in forecasting the stock price ...
Afandi W. Biga, Isran K. Hasan, Nurwan
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This study aims to compare the effectiveness of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model and the Autoregressive Integrated Moving Average–Feedforward Neural Network (ARIMA-FFNN) hybrid model in forecasting the stock price ...
Afandi W. Biga, Isran K. Hasan, Nurwan
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Using the ARFIMA-RBFN Hybrid Model to Forecast Oil Prices
Advances in Nonlinear Variational InequalitiesThis study dealt with the analysis of oil price time series for the period (1960-2023) obtained from World Bank data, to enhance forecasting accuracy of future values. Three primary models were utilized: The first model Auto Regressive Fractionally
Mukhtar Hussein +5 more
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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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