Intervention Modelling of Crude Oil Prices Using Pre- and Post-Transfer Function Models
Main Article Content
Abstract
In order to model Nigerian crude oil prices, the study used Autoregressive Integrated Moving Average Intervention models. The rapid decline in the series was manifest in the time plot, necessitating an intervention process. The data were separated into pre-intervention and post-intervention series. At order one, the series was examined for stationarity. Four models were estimated based on AIC. There were pre- and post-intervention sections. To reduce the erroneous correlation effect between the pre- and post-intervention data, they were pre-whitened. The pre-whitened input series (pre-intervention series) and output series (post-intervention series) were both pre-whitened, and the cross-correlation between the two was investigated. The cross-correlation behaviors were used to generate a rational polynomial representation for the dynamic transfer function models. It was revealed that the calculated noise was autocorrelated. This filled up the gaps in the transfer function model, which was then used to fit the entire model. The resulting model was subjected to a diagnostic test and determined to be suitable.
Article Details
References
Arumugam, P., & Anithakumari, V. (2013). Seasonal time series and transfer function modelling for Natural Rubber Forecasting in India. International Journal of Computer Trends and Technology. 4(1),1366-1370. https://ijcttjournal.org/archives/ijctt-v4i5p78
Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control. ;19(6):716–723. https://link.springer. com/chapter/10.1007/978-1-4612-1694-0_16
Box, GEP, Jenkins, GM, &Reinsel, GC. (1994) Time series analysis, forecasting and control, 3rd Ed. Prentice Hall Enjlewood Clits;
Central Bank of Nigeria. Statistical Bulletin; 2024. Available: http;/www.centralbank.org
Chung, RCP., Ip WH., &Chan S.L. (2009). An ARIMA-intervention analysis model for the financial crisis in China’s manufacturing industry. International Journal of Engineering Business Management. 1(1):15-18. https://link.springer.com/article/10.1007/s00170-008-1792-y
Etuk, E. H. (2012). Multiplicative SARIMA modelling of daily Naira Euro exchange rates.International Journal of mathematics and statistics studies 1(3) 3. https://www.researchgate.net/publication/316918917_A_Model_for_Daily_Exchange_Rates_of_the_Naira_and_the_XOF_by_Seasonal_ARIMA_Methods
Gujarati, D.N. (2013). Basic econometrics. McGraw-Hill: Glasgow.
Iwok, I. A. (2016). A Transfer function –autoregressive Noise of Naira Exchange Rate for US Dollar and Swiss Franc. Research & Reviews: Journal of Statistics and Mathematical Science. https://www.rroij.com/open-access/a-transfer-functionautoregressive-noise-model-of-naira-exchangerates-for-us-dollar-and-swiss-franc-.php?aid=69697
Mishr,A D., Padmanaban K., Dikale, B.S,& Tailor AK.(2018). Statistical investigation of the production performance of cumin in India. Economic Affairs.63(2):1-9. https://scispace.com/pdf/statistical-investigation-of-production-performance-of-cumin-4ls2szlye1.pdf
Ntebogang, D. M. (2015). Box-Jenkins Transfer Function Framework Applied To Saving-Investment Nexus In The South African Context. Journal of Governance and Regulation. 4,(1). https://virtusinterpress.org/BOX-JENKINS-TRANSFER-FUNCTION.html
Oyefusi A. (2007). Oil and gas, the propensity to armed struggle in the Niger Delta Region of Nigeria. World Bank Policy Research Working Paper No. 4194;. https://openknowledge.worldbank.org/entities/publication/d96fa720-46c9-5a3c-bd66-1e7508e6e467
Pong-Wai, L. (1979). Transfer Function Modelling Relationship Between Time Series Variables. London School of Economics and Political Science. https://uci.primo.exlibrisgroup.com/discovery/fulldisplay?vid=01CDL_IRV_INST:UCI&docid=alma991012950629704701&lang=en&context=L&adaptor=Local%20Search%20Engine
Victor-Edema, U.A., & Essi, I.D. (2020). A Transfer Function Modelling of Nigeria's Current Account (net) and Exchange Rate. International Journal of Statistics and Applied Mathematics. 5(4)177-185. https://www.researchgate.net/publication/363536386_TRANSFER_FUNCTION_MODELLING_OF_INFLATION_RATE_AND_IMPORT_DUTIES_IN_NIGERIA
Wiri, L, Essi, I.D. (2018). Seasonal autoregressive integrated moving average (SARIMA) modelling and forecasting of the inflation rate in Nigeria. International Journal of Applied Science and Mathematical Theory. ;4(1):1-14. https://www.researchgate.net/publication/346921749_Seasonal_Autoregressive_Integrated_Moving_Average_SARIMA_Modelling_and_Forecasting_of_Inflation_Rates_in_Nigerian_2003-_2016
Wiri, L., &Tuaneh, G. B. (2019). Modelling Nigeria Crude Oil Prices Using ARIMA, Pre-Intervention and Post-Intervention Model. Asian Journal of Probability and Statistics. 3(1). https://journalajpas.com/index.php/AJPAS/article/view/59
Wiri. L., &Rechard, C.L. (2022). Transfer function modelling of inflation rate and import duties in Nigeria. Royal Statistical Society Nigeria Local Group 3(64-72). https://www.researchgate.net/publication/363536386_TRANSFER_FUNCTION_MODELLING_OF_INFLATION_RATE_AND_IMPORT_DUTIES_IN_NIGERIA