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Dynamic Modeling Using Vector Error-correction Model: Studying the Relationship among Data Share Price of Energy PGAS Malaysia, AKRA, Indonesia, and PTT PCL-Thailand

Warsono Warsono, Edwin Russel, Almira Rizka Putri, Wamiliana Wamiliana, Widiarti Widiarti and Mustofa Usman
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Warsono Warsono: Department of Mathematics, Faculty of Science and Mathematics, Universitas Lampung, Indonesia,
Edwin Russel: Department of Management, Faculty of Economic and Business, Universitas Lampung, Indonesia.
Almira Rizka Putri: 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,

International Journal of Energy Economics and Policy, 2020, vol. 10, issue 2, 360-373

Abstract: Vector Error-Correction Model (VECM) is a method of statistical analysis frequently used in many studies in time series data of economy, business and finance, and data energy. It is applied across researches due to its simplicity and limited restrictions. VECM can explain not only the dynamic behavior of the relationship among variables of endogenous and exogenous, but also among the endogenous variables. Moreover, it also explains the impact of a variable or a set of variables on others by means of Impulse Response Function and Granger Causality analysis. It can also be used for forecasting multivariate time series data. In this research, the relationship of three share price of energy (from three Asean countries: PGAS Malaysia, AKRA Indonesia, and PTT Thailand) will be studied. The data in this study were collected from October 2005 to August 2019. Based on the comparison of some VECM models, it was found that the best model is VECM (2) with cointegration rank=3. The dynamic behavior of the data is studied through Impulse Response Function (IRF), Granger Causality analysis and forecasting for the next five periods(weeks).

Keywords: Cointegration; VAR model; VECM model; Granger Causality; Impulse Response Function; Forecasting. (search for similar items in EconPapers)
JEL-codes: C32 Q4 Q47 (search for similar items in EconPapers)
Date: 2020
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