@Article{cmc.2020.08901,
AUTHOR = {Ibrahim Mufrah Almanjahie, Zouaoui Chikr Elmezouar, 3, *, 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}
}