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Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America

Received: 20 January 2017     Accepted: 19 September 2017     Published: 5 November 2017
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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.

Published in Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 5)
DOI 10.11648/j.sjams.20170505.12
Page(s) 174-180
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Transformation, SARIMA, Unit Root, Crude Oil Export, ARCH-LM

References
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[6] Kumar Manoj and Anand Manhu (2012). An application of time Series ARIMA Forecasting model for predicting sugarcane production in India. Studies in Business and Economics. 81-94.
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[8] Smart (2013). Modelling and Forecasting Maternal Mortality; An Application of ARIMA Models, International Journal of Applied Science and Technology. 3(1): 19 – 28.
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Cite This Article
  • APA Style

    Obinna Adubisi, Titus Terkaa Mom, Chidi Emmanuel Adubisi, Phillip Luka. (2017). Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America. Science Journal of Applied Mathematics and Statistics, 5(5), 174-180. https://doi.org/10.11648/j.sjams.20170505.12

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    ACS Style

    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. Sci. J. Appl. Math. Stat. 2017, 5(5), 174-180. doi: 10.11648/j.sjams.20170505.12

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    AMA Style

    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. Sci J Appl Math Stat. 2017;5(5):174-180. doi: 10.11648/j.sjams.20170505.12

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  • @article{10.11648/j.sjams.20170505.12,
      author = {Obinna Adubisi and Titus Terkaa Mom and Chidi Emmanuel Adubisi and Phillip Luka},
      title = {Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {5},
      number = {5},
      pages = {174-180},
      doi = {10.11648/j.sjams.20170505.12},
      url = {https://doi.org/10.11648/j.sjams.20170505.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20170505.12},
      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.},
     year = {2017}
    }
    

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    T1  - Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America
    AU  - Obinna Adubisi
    AU  - Titus Terkaa Mom
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    Y1  - 2017/11/05
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    DO  - 10.11648/j.sjams.20170505.12
    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
    SP  - 174
    EP  - 180
    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20170505.12
    AB  - 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.
    VL  - 5
    IS  - 5
    ER  - 

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Author Information
  • Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria

  • Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria

  • Department of Physics, Faculty of Physical Science, University of Ilorin, Ilorin, Nigeria

  • Department of Mathematics and Statistics, Faculty of Pure & Applied Sciences, Federal University Wukari, Wukari, Nigeria

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