Volume 5, Issue 5, October 2017, Page: 174-180
Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America
Obinna Adubisi, Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria
Titus Terkaa Mom, Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria
Chidi Emmanuel Adubisi, Department of Physics, Faculty of Physical Science, University of Ilorin, Ilorin, Nigeria
Phillip Luka, Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria
Received: Jan. 20, 2017;       Accepted: Sep. 19, 2017;       Published: Nov. 5, 2017
DOI: 10.11648/j.sjams.20170505.12      View  1409      Downloads  56
Abstract
In the last few decades, crude oil has claimed the topmost position in Nigerian export list, constituting a very fundamental change in the structure of Nigerian international trade. In this study, secondary data on monthly crude oil export to the United States was obtained from the Energy Information Administration (EIA) database. Using the Box-Jenkins (ARIMA) methodology, the results showed that Seasonal ARIMA (0, 1, 1) (1, 0, 1)12 model had the least information criteria after the data was Square-Root transformed and non-seasonally first differenced in order to achieve series stationarity. The diagnostic tests on the selected model residuals revealed the residuals are normally distributed uncorrelated random shocks.
Keywords
Transformation, SARIMA, Unit Root, Crude Oil Export, ARCH-LM
To cite this article
Obinna Adubisi, Titus Terkaa Mom, Chidi Emmanuel Adubisi, Phillip Luka, Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America, Science Journal of Applied Mathematics and Statistics. Vol. 5, No. 5, 2017, pp. 174-180. doi: 10.11648/j.sjams.20170505.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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