@Article{cmc.2020.08901,
AUTHOR = {Ibrahim Mufrah Almanjahie, Zouaoui Chikr Elmezouar, Ali Laksaci},
TITLE = {Functional Causality between Oil Prices and GDP Based on Big  Data},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {593--604},
URL = {http://www.techscience.com/cmc/v63n2/38531},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.08901}
}