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Modeling Long Memory Volatilities of Nigeria Selected Macro Economic Variables with Arfima and Arfima Figarch

open access: yesCumhuriyet Science Journal
The research delved into analysing the stochastic characteristics of Nigeria's Real GDP, the exchange rate of the Naira to US Dollar, and the inflation rate employing Autoregressive fractionally integrated moving average (ARFIMA) and the Autoregressive ...
Ayoade Adewole
doaj   +5 more sources

IDENTIFICACIÓN DE MODELOS ARFIMA

open access: yesRevista de la Facultad de Ciencias, 2016
Desde la introducción de los modelos fraccionalmente integrados ARFIMA para  series de tiempo con memoria larga, ha surgido un gran interés en el estudio de sus propiedades y áreas de aplicación.
Elkin Castaño-Vëlez
doaj   +6 more sources

Hybridization of long short-term memory neural network in fractional time series modeling of inflation [PDF]

open access: yesFrontiers in Big Data, 2023
Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates.
Erman Arif   +4 more
doaj   +2 more sources

Considerations for Applying Entropy Methods to Temporally Correlated Stochastic Datasets [PDF]

open access: yesEntropy, 2023
The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative
Joshua Liddy, Michael Busa
doaj   +2 more sources

Forecasting Realised Volatility Using ARFIMA and HAR Models [PDF]

open access: yesQuantitative 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.
Mohammad Kabir Hassan   +2 more
exaly   +3 more sources

Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis [PDF]

open access: yesJournal of Translational Medicine
Background The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain.
Yongbin Wang   +8 more
doaj   +2 more sources

التنبؤ باستخدام نموذج الانحدار الذاتي والمتوسطات المتحركة التكاملية الكسرية ARFIMA (p, d, q) : دراسة تطبيقية [PDF]

open access: yesالمجلة العلمية للدراسات والبحوث المالية والتجارية, 2023
                   تم في هذا البحث دراسة وعرض خطوات تحليل السلاسل الزمنية باستخدام نموذج الانحدار الذاتي والمتوسطات المتحركة التكاملية الكسرية "نموذج السلاسل الزمنية ذات الذاكرة الطويلة" ARFIMA (p, d, q).
أمال عبد الغني   +1 more
doaj   +1 more source

The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data

open access: yesFrontiers in Applied Mathematics and Statistics, 2022
IntroductionThe price of crude oil as an essential commodity in the world economy shows a pattern and identifies the component factors that influence it in the short and long term.
Dodi Devianto   +4 more
doaj   +1 more source

ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19 [PDF]

open access: yesE3S Web of Conferences, 2020
COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects.
Kartikasari Puspita   +2 more
doaj   +1 more source

BOOTSTRAP ASSISTED SPECIFICATION TESTS FOR THE ARFIMA MODEL [PDF]

open access: yesEconometric Theory, 2011
This paper proposes bootstrap assisted specification tests for the autoregressive fractionally integrated moving average model based on the BartlettTp-process with estimated parameters whose limiting distribution under the null depends on the estimated model and the estimation method employed.
Delgado, Miguel A.   +2 more
openaire   +2 more sources

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