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Analysis of Data Inflation Energy and Gasoline Price by Vector Autoregressive Model

Author

Listed:
  • Nairobi Nairobi

    (Department of Economic Development, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Ambya Ambya

    (Department of Economic Development, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Edwin Russel

    (Department of Economic Development, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Sipa Paujiah

    (Department of Economic Development, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • D. N. Pratama

    (Department of Economic Development, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Wamiliana Wamiliana

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia)

  • Mustofa Usman

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia)

Abstract

The study of multivariate time series data analysis has become many topics of research in the fields of economics and business. In the present study, we will analyze data energy inflation and gasoline prices of Indonesia over the years from 2014 to 2020. The purpose of this study is to obtain the best model of the dynamic relationship between inflation and gasoline prices. The dynamic modeling that will be used in this research is modeling using the Vector Autoregressive (VAR) model. From the analysis results, the best model is the VAR model with order 3 (p=3), VAR(3). Based on the best model, VAR(3), further studies will be discussed with regard to Granger causality analysis, Impulse Response Function, and Forecasting.

Suggested Citation

  • 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.
    • Handle: RePEc:eco:journ2:2022-02-12
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    AICC; VAR(p) model; Granger causality; Impulse Response Function; Forecasting;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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