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,Almira Rizka Putri,Widiarti Widiarti,Edwin Russel,Mustofa Usman,Wamiliana Wamiliana

doi:10.32479/ijeep.8946

Warsono Warsono, Almira Rizka Putri + Show 4 more

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https://doi.org/10.32479/ijeep.8946

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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). Keywords: Cointegration, Vector Autoregressive Model, VECM Model, Granger Causality, Impulse Response Function, Forecasting JEL Classifications: C32, Q4, Q47

Highlights

The study of energy economics as a research area is being conducted by many researchers, especially due to the existing problems regarding energy, including lack of energy and renewable energy (Iazzolino et al, 2019; Forero et al, 2019; Warsono et al, 2019a; 2019b)
Yu et al (2008) used error-correction model, cointegration analysis, and impulse response function (IRF) to discuss the connection between Economic Growth and China Energy
If the set of data time series has cointegration, the vector autoregressive (VAR) model has to be modified into vector error-correction model (VECM)

Summary

Introduction

The study of energy economics as a research area is being conducted by many researchers, especially due to the existing problems regarding energy, including lack of energy and renewable energy (Iazzolino et al, 2019; Forero et al, 2019; Warsono et al, 2019a; 2019b). Pala (2013) investigated the relationship between food price index and crude oil price using VECM modeling. Warsono et al (2019a) discussed the relationship and forecasting between the price indexes of two coal companies in Indonesia using the vector autoregressive (VAR) model. Campiche et al (2007) discussed the relationship between crude oil prices and agricultural commodities prices by using cointegration and vector error-correction model (VECM). Yu et al (2008) used error-correction model, cointegration analysis, and IRF to discuss the connection between Economic Growth and China Energy. The VAR model was introduced by Sims (1980) as a method to analyze macroeconomic data. He developed the VAR model as an alternative to the traditional system of simultaneous equation methods (Kirchgassner and Wolters, 2007). If the set of data time series has cointegration, the VAR model has to be modified into VECM

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