Keywords and phrases: time series models, forecasting, crude oil, COVID-19, E-views program.
Received: January 8, 2023; Revised: February 10, 2023; Accepted: February 15, 2023; Published: March 22, 2023
How to cite this article: Mahmoud M. Abdelwahab, Time series models to forecast Brent crude oil prices and the impact of COVID-19 using E-views program, Advances and Applications in Statistics 86(2) (2023), 141-155. http://dx.doi.org/10.17654/0972361723020
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] M. Aamir and A. B. Shabri, Modelling and forecasting monthly crude oil price of Pakistan: a comparative study of ARIMA, GARCH and ARIMA Kalman model, AIP Conf. Proc. 1750 (2016), 060015. [2] H. Akaike, Information theory and an extension of the maximum likelihood principle, B. N. Petrov and F. Caskie, eds., 2nd International Symposium on Information Theory, Académiai Kiadó, Budapest, Hungary, 1973, pp. 267-281. [3] R. S. Al-Gounmeen and M. T. Ismail, Forecasting the exchange rate of the Jordanian Dinar versus the US Dollar using a Box-Jenkins seasonal ARIMA model, Int. J. Math. Comput. Sci. 15(1) (2020), 27-40. [4] D. Asteriou and S. Hall, Applied Econometrics: A Modern Approach, Palgrave Macmillan, New York, 2007. [5] A. Bahar, N. M. Noh and Z. M. Zainuddin, Forecasting model for crude oil price with structural break, Malaysian Journal of Fundamental and Applied Sciences (2017), 421-424. [6] B. Fazelabdolabadi, A hybrid Bayesian-network proposition for forecasting the crude oil price, Financial Innovation 5(30) (2019), 1-21. [7] X. J. He, Crude oil prices forecasting, time series vs. SVR models, Journal of International Technology and Information Management 27(2) (2018), 25-42. [8] M. T. Ismail and A. M. Awajan, A new hybrid approach EMD-EXP for short-term forecasting of daily stock market time series data, Electronic Journal of Applied Statistical Analysis 10(2) (2017), 307-327. [9] C. Y. Lee and S. Y. Huh, Forecasting long-term crude oil prices using a Bayesian model with informative priors, Sustainability 9 (2017), 190. [10] J. G. MacKinnon, Numerical distribution functions for unit root and co-integration tests, J. Appl. Econometrics 11 (1996), 601-618. [11] C. Manescu and I. Van Robays, Forecasting the Brent oil price: addressing time, variation in forecast performance, CESifo Working Paper 6242, 2016. [12] A. Nyangarika, A. Mikhaylov and U. H. Richter, Oil price factors: forecasting on the base of modified auto-regressive integrated moving average model, International Journal of Energy Economics and Policy 9(1) (2019), 149-159. [13] G. Schwarz, Estimating the dimension of a model, Ann. Statist. 6 (1978), 461-464. [14] N. Sehgal and K. K. Pandey, Artificial Intelligence Methods for Oil Price Forecasting, A Review and Evaluation, Springer-Verlag Berlin Heidelberg, 2015. [15] S. Strom and B. Beackers, Forecasting the Nominal Brent Oil Price with VARs-One Model Fits All, International Monetary Fund, 2015, pp. 122-134. [16] X. Yin, J. Peng and T. Tang, Improving the forecasting accuracy of crude oil prices, Sustainability 10 (2018), 454. [17] L. Yu, S. Wang and K. K. Lai, Forecasting crude oil price with an EMD based neural network ensemble learning paradigm, Energy Economics 30 (2008), 2623 2635. [18] L. Yu, X. Zhang and S. Wang, Assessing potentiality of support vector machine method in crude oil price forecasting, EURASIA Journal of Mathematics, Science and Technology Education 13(12) (2017), 7893-7904.
|