R package arfurima for fractional unit root integral (FURI) time series, ARFIMA and ARFURIMA models

Jibrin, Sanusi Alhaji; Rahman, Rosmanjawati Abdul

doi:10.1063/1.5136403

Cited by 1 publication

(1 citation statement)

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“…Many R Packages developed in the literatures are synonyms to the targeted models and they could be classified as mini software (see R Package arfima in Veenstra (2012), artfima in McLeod et al, (2016) and arfurima package of Jibrin and Rahman (2019)). There are other few packages that implement the mean, volatility and hybrid models.…”

Section: Related Workmentioning

confidence: 99%

New R package arfurimaaparch for Estimation of ARFURIMA-APARCH Model and Big Data Analytics

Jibrin¹,

Ibrahim²

2022

SLUJST

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This paper introduces the R package arfurimaaparch version 0.1.0 for time series computations, big data analytics and estimation of Autoregressive Fractional Unit Root Integral Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The fdr, arfurimaaparch, arfurimaaparchforecast, arfurimaaparchdiagnostic and arfurimaaparch.sim are the main functions of the package. An improved version of the arfurima package version 1.1.0 of Jibrin and Rahman (2019) for implementing Monte Carlo simulation is also presented. Daily Nigeria all share index and West Texas Intermediate (WTI) crude oil prices for the period 26th January 2004 to 31st December 2018 were used to explained the usage of the packages. When the arfurimaaparch package is compared with other long memory packages, It would produce better stationary process after transformation, appropriate fractional differencing values in the interval of , minimum Akaike Information Criteria values, larger log-likelihood values, minimum p-values of the ARFURIMA-APARCH parameters estimates and large p-values of the Ljung-Box, ARCH-LM and Jarque-Bera test. Findings show that both R packages and their functions are robust, simple and user-friendly. As conclusion, the R packages are suitable, good and reliable for time series analysis computations, statistical analysis and big data analytics.

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Section: Related Workmentioning

confidence: 99%