Dynamic Modeling Using Vector Error-Correction Model: Studying the Relationship Among Data Share Price of Energy Pgas Malaysia, Akra, Indonesia, and PTT PCL-Thailand

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 (IRF) 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 IRF, Granger Causality analysis and forecasting for the next five periods (weeks).

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

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The Impact of Foreign Direct Investment on Poverty Reduction: Evidence from Bangladesh

“…However, there is multicollinearity among the independent variables which commonly exist in the time series data. But one of the advantages of VECM is that it reduces the multicollinearity in the error correction form (Warsono, et al, 2020).…”

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

“…The ADF test checks the stationary data with the null hypothesis that the data are nonstationary (Fuller, 1985;Wei, 2006;Warsono et al, 2020). The ADF test with lag-p is formulated as follows (Zivot and Wang, 2006;Tsay, 2010):…”