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Dynamic Modeling Data Export Oil and Gas and Non-Oil and Gas by ARMA(2,1)-GARCH(1,1) Model: Study of Indonesian s Export over the Years 2008-2019

Author

Listed:
  • Nairobi Nairobi

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

  • Edwin Russel

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

  • Ambya Ambya

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

  • Arif Darmawan

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

  • Mustofa Usman

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

  • Wamiliana Wamiliana

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

Abstract

It is well known that a country's economy is very dependent on the export of goods and services produced by that country. This depends on exporting either mining products such as oil and gas or non-oil and gas. This paper will study the data export of oil and gas and data export of non-oil and gas of Indonesian over the years 2008 to 2019. The aim of this study is to obtain the best model that can describe the pattern of the data export of oil and gas and data export of non-oil and gas. From the results of the analysis, researchers found that the best models that can describe the pattern of data export of oil and gas and data export of non-oil and gas are the same, namely: ARMA (2.1) -GARCH (1.1) models. These models for both data are very significant with P-values

Suggested Citation

  • Nairobi Nairobi & Edwin Russel & Ambya Ambya & Arif Darmawan & Mustofa Usman & Wamiliana Wamiliana, 2020. "Dynamic Modeling Data Export Oil and Gas and Non-Oil and Gas by ARMA(2,1)-GARCH(1,1) Model: Study of Indonesian s Export over the Years 2008-2019," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 175-184.
    • Handle: RePEc:eco:journ2:2020-06-23
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    References listed on IDEAS

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    1. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. K. Hsieh & P. Ritchken, 2005. "An empirical comparison of GARCH option pricing models," Review of Derivatives Research, Springer, vol. 8(3), pages 129-150, December.
    4. Hung‐Hsi Huang & Yung‐Ming Shiu & Pei‐Syun Lin, 2008. "HDD and CDD option pricing with market price of weather risk for Taiwan," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(8), pages 790-814, August.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Chia Chun Lo & Konstantinos Skindilias & Andreas Karathanasopoulos, 2016. "Forecasting Latent Volatility through a Markov Chain Approximation Filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 54-69, January.
    7. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Akaike s Information Criterion; Autoregressive Moving Average; Generalized Autoregressive Conditional Heteroscedasticity; Mean Average Percentage Error.;
    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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