Pattern prediction of crude oil using regression moderated with Markov switching model

Zhou, Ding and Qazi, Atika and Raj, Ram Gopal and Liew, Haw Ling and Eledo, Celestine O. (2019) Pattern prediction of crude oil using regression moderated with Markov switching model. Malaysian Journal of Computer Science, 32 (2). pp. 149-162. ISSN 0127-9084, DOI

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Scarcity of refined products for long time does contribute to economic backwardness and de-industrialization of most sub-Saharan African and the Organization of Petroleum Exporting Countries (OPEC). Institutional quality and managerial transparency absence impacted the downstream sub-sector negatively and induces Nigeria to import 80% refined products despite its huge crude oil exports. We have used Markov-switching models, a stochastic technique capable of capturing statistical knowledge to moderate Newton Series Polynomial generated on a best linear slope equation. And postulate that with less than 70% annual refinery utilization and undemocratic institutional performance, the Nigerian state will continue to experience resource curse syndrome. Continuation of Nigeria's mono-product economic structure having demonstrated a dismal performance to the economy may curse doom for the nation if it ignores calls for more domestically refined products. This paper offers an oil utilization directional guide to economic development in oil rich nations as against historical illustration of resources rich nations' failure to develop fast. However, if Nigeria chooses to maintain its current crude oil exports status industrialization is foregone.

Item Type: Article
Uncontrolled Keywords: Downstream sub-sector; Industrialization; Institutions; Markov-switching regimes; Oil utilization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Computer Science & Information Technology
Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 31 Oct 2019 08:41
Last Modified: 31 Oct 2019 08:41

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