IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2019-04-30.html
   My bibliography  Save this article

Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia over the Years 2002-2017)

Author

Listed:
  • Warsono Warsono

    (Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia.)

  • Edwin Russel

    (Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia.)

  • Wamiliana Wamiliana

    (Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia.)

  • Widiarti Widiarti

    (Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia.)

  • Mustofa Usman

    (Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia.)

Abstract

The vector autoregressive moving average (VARMA) model is one of the statistical analyses frequently used in several studies of multivariate time series data in economy, finance, and business. It is used in numerous studies because of its simplicity. Moreover, the VARMA model can explain the dynamic behavior of the relationship among endogenous and exogenous variables or among endogenous variables. It can also explain the impact of a variable or a set of variables by means of the impulse response function and Granger causality. Furthermore, it can be used to predict and forecast time series data. In this study, we will discuss and develop the best model that describes the relationship between two vectors of time series data export of Coal and data export of Oil in Indonesia over the period 2002 2017. Some models will be applied to the data: VARMA (1,1), VARMA (2,1), VARMA (3,1), and VARMA (4,1). On the basis of the comparison of these models using information criteria AICC, HQC, AIC, and SBC, it was found that the best model is VARMA (2,1) with restriction on some parameters: AR2_1_2=0, AR2_2_1=0, and MA1_2_1=0. The dynamic behavior of the data is studied through Granger causality analysis. The forecasting of the series data is also presented for the next 12 months.

Suggested Citation

  • Warsono Warsono & Edwin Russel & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2019. "Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia over the Years 2002-2017)," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 240-247.
    • Handle: RePEc:eco:journ2:2019-04-30
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/7605/4431
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/7605/4431
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vadim Faruarovich Islamutdinov & Evgeniy Igorevich Kushnikov, 2020. "Long-term Forecast of the Dependence of the Economy of the Khanty-Mansi Autonomous Okrug-Ugra (Russia) on the Sectors of the Fuel and Energy Complex," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 382-389.
    2. Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022. "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 178-191, September.
    3. Mustofa Usman & M. Komarudin & Nurhanurawati Nurhanurawati & Edwin Russel & Ahmad Sidiq & Warsono Warsono & F. A.M Elfaki, 2023. "Dynamic Modeling and Analysis of Some Energy Companies of Indonesia Over the Year 2018 to 2022 By Using VAR(p)-CCC GARCH(r,s) Model: -," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 542-554, July.
    4. Warsono Warsono & Edwin Russel & Almira Rizka Putri & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2020. "Dynamic Modeling Using Vector Error-correction Model: Studying the Relationship among Data Share Price of Energy PGAS Malaysia, AKRA, Indonesia, and PTT PCL-Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 360-373.
    5. Nairobi Nairobi & Ambya Ambya & Edwin Russel & Sipa Paujiah & D. N. Pratama & Wamiliana Wamiliana & Mustofa Usman, 2022. "Analysis of Data Inflation Energy and Gasoline Price by Vector Autoregressive Model," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 120-126, March.
    6. Mustofa Usman & Luvita Loves & Edwin Russel & Muslim Ansori & Warsono Warsono & Widiarti Widiarti & Wamiliana Wamiliana, 2022. "Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 91-102, March.
    7. Rialdi Azhar & Fajrin Satria Dwi Kesumah & Ambya Ambya & Febryan Kusuma Wisnu & Edwin Russel, 2020. "Application of Short-term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, JII)," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 294-301.
    8. Sri Hasnawati & Mustofa Usman & Ahmad Faisol & Faiz A. M. Elfaki, 2023. "Analysis and Modeling Gross Domestic Product, Carbon Dioxide Emission, Population Growth, and Life Expectancy at Birth: Case Study in Qatar," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 467-483, March.

    More about this item

    Keywords

    VARMA model; Information criteria; Granger causality; Forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2019-04-30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.