Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins

Ansari Saleh Ahmar; Miguel Botto-Tobar; Abdul Rahman; Rahmat Hidayat

doi:10.35877/454RI.jinav260

Authors

Ansari Saleh Ahmar Department of Statistics, Universitas Negeri Makassar, Makassar, 90223, Indonesia http://orcid.org/0000-0001-6888-9043
Miguel Botto-Tobar (1) Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (2) Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, 090510, Guayaquil, Ecuador https://orcid.org/0000-0001-7494-5224
Abdul Rahman Department of Mathematics, Universitas Negeri Makassar, Makassar, 90223, Indonesia
Rahmat Hidayat Department of Information Technology, Politeknik Negeri Padang, Limau Manis, Padang, 25164, Indonesia

DOI:

https://doi.org/10.35877/454RI.jinav260

Keywords:

ARIMA, forecasting, oil and gas export

Abstract

The objective of the study was to forecast the value of oil and gas exports in Indonesia using the ARIMA Box-Jenkins. With this prediction, it is hoped that it can be a study for future policy making. This oil and gas export data is obtained from the Indonesian Central Bureau of Statistics (BPS) website, in raw data from January 2010 to March 2022. This data is predicted using the ARIMA method with the help of R software. The stages of data analysis with ARIMA include: data stationary test, build the model indication, parameter estimation and significance test, and residual diagnostic test of the model. The results of data analysis conducted in this study show that there are 3 indications of models that were generated, namely ARIMA(1,1,0); ARIMA(0,1,1); and ARIMA(1,1,0). From these 3 model indications, the best model was ARIMA(0,1,1) with AIC value of 2047.65.

References

Ahmar, A. S., Guritno, S., Rahman, A., Minggi, I., Tiro, M. A., Aidid, M. K., ... & Arifin, A. N. M. (2018). Modeling data containing outliers using ARIMA additive outlier (ARIMA-AO). In Journal of Physics: Conference Series (Vol. 954, No. 1, p. 012010). IOP Publishing. https://doi.org/10.1088/1742-6596/954/1/012010

Andrianto, R. (2022). 10 Negara Penghasil Minyak Bumi Terbesar, Ada Indonesia? [The 10 biggest petroleum producing countries, is there Indonesia?]. Retrieved from https://www.cnbcindonesia.com/market/20220906162442-17-369783/10-negara-penghasil-minyak-bumi-terbesar-ada-indonesia

Badan Pusat Statistik. (2022). Nilai Ekspor Migas-NonMigas (Juta US$), 2022. Retrieved from https://bps.go.id/indicator/8/1753/1/nilai-ekspor-migas-nonmigas.html

ESDM. (2013). Indonesia Nomor 5 Dunia Dalam Porsi Bagi Hasil Migas Untuk Negara [Indonesia is the 5th in the world in the allocation of oil and gas revenue to the state]. Retrieved from https://www.esdm.go.id/id/media-center/arsip-berita/indonesia-nomor-5-dunia-dalam-porsi-bagi-hasil-migas-untuk-negara

Kasyok, A. (2013). Simple steps for fitting ARIMA model to time series data for forecasting using R. International Journal of Science and Research (IJSR), 4(3), 318-321.

Mushtaq, R. (2011). Augmented Dickey Fuller Test. Available at SSRN: https://ssrn.com/abstract=1911068 or http://dx.doi.org/10.2139/ssrn.1911068

Osborne, J. (2010). Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research, and Evaluation, 15(12), 1-8.

Salsabila, D. (2021). Analisis Pengaruh Ekspor Migas dan Non Migas terhadap Pertumbuhan Ekonomi Indonesia. Jurnal Akuntansi Dan Manajemen, 18(01), 01-08. https://doi.org/10.36406/jam.v18i01.374