O. Adubisi, Titus Terkaa Mom, C. Adubisi, Phillip Luka
{"title":"Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America","authors":"O. Adubisi, Titus Terkaa Mom, C. Adubisi, Phillip Luka","doi":"10.11648/J.SJAMS.20170505.12","DOIUrl":null,"url":null,"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.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Journal of Applied Mathematics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11648/J.SJAMS.20170505.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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.