Table of Content

Open Access

ARTICLE

Functional Causality between Oil Prices and GDP Based on Big Data

Ibrahim Mufrah Almanjahie1, 2, Zouaoui Chikr Elmezouar1, 2, 3, *, Ali Laksaci1, 2
1 Department of Mathematics, College of Science, King Khalid University, Abha, 62529, Saudi Arabia.
2 Statistical Research and Studies Support Unit, King Khalid University, Abha, 62529, Saudi Arabia.
3 Department of Mathematics, University Tahri Mohamed, Bechar, 8000, Algeria.
* Corresponding Author: Zouaoui Chikr Elmezouar. Email: .

Computers, Materials & Continua 2020, 63(2), 593-604. https://doi.org/10.32604/cmc.2020.08901

Received 23 October 2019; Accepted 09 January 2020; Issue published 01 May 2020

Abstract

This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We discuss the causality in the sense of Granger, which requires the stationarity of the data. Thus, in the first Step, we test the stationarity by using the Monte Carlo test of a functional time series stationarity. Our main goal is treated in the second step, where we use the functional causality idea to model the co-variability between these variables. We show that the two series are not integrated; there is one causality between these two variables. All the statistical analyzes were performed using R software.

Keywords

Functional time series, functional stationarity, FAR, FARX, causality.

Cite This Article

I. Mufrah Almanjahie, Z. Chikr Elmezouar and A. Laksaci, "Functional causality between oil prices and gdp based on big data," Computers, Materials & Continua, vol. 63, no.2, pp. 593–604, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1384

    View

  • 991

    Download

  • 0

    Like

Related articles

Share Link

WeChat scan