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Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy

Author

Listed:
  • Warsono Warsono

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

  • Edwin Russels

    (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

Owing to its simplicity and less restrictions, the vector autoregressive with exogenous variable (VARX) model is one of the statistical analyses frequently used in many studies involving time series data, such as finance, economics, and business. The VARX model can explain the dynamic behavior of the relationship between endogenous and exogenous variables or of that between endogenous variables only. It can also explain the impact of a variable or a set of variables on others through the impulse response function (IRF). Furthermore, VARX can be used to predict and forecast time series data. In this study, PTBA and HRUM energy as endogenous variables and exchange rate as an exogenous variable were studied. The data used herein were collected from January 2014 to October 2017. The dynamic behavior of the data was also studied through IRF and Granger causality analyses. The forecasting data for the next one month was also investigated. On the basis of the data provided by these different models, it was found that VARX (3,0) is the best model to assess the relationship between the variables considered in this work.

Suggested Citation

  • Warsono Warsono & Edwin Russels & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2019. "Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 390-398.
    • Handle: RePEc:eco:journ2:2019-02-45
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    References listed on IDEAS

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    Cited by:

    1. Walid Mansour & Hechem Ajmi & Karima Saci, 2022. "Regulatory policies in the global Islamic banking sector in the outbreak of COVID-19 pandemic," Journal of Banking Regulation, Palgrave Macmillan, vol. 23(3), pages 265-287, September.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wamiliana Wamiliana & Edwin Russel & Iskandar Ali Alam & Widiarti Widiarti & Tuti Hairani & Mustofa Usman, 2024. "Modeling and Forecasting Closing Prices of some Coal Mining Companies in Indonesia by Using the VAR(3)-BEKK GARCH(1,1) Model," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 579-591, January.
    8. 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.
    9. Sri Hasnawati & Mustofa Usman & Faiz AM Elfaki & Ahmad Faisol & Edwin Russel, 2024. "Modeling the Relationship between Life Expectancy, Population Growth, Carbon Dioxide Emission, and GDP Growth in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 484-500, July.
    10. Andrew K. Kamenju & Dr. T. Olweny, 2021. "The Nexus Between Internal Investment and Economic Growth in Kenya," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 11(2), pages 1-2.
    11. 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.

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    More about this item

    Keywords

    VAR model; VARX model; Granger causality; Impulse Response Function; Forecasting.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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