Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study From Indonesia Over the Years 2002-2017)

Abstract

The vector autoregressive moving average (VARMA) model is one of the statistical analyses frequently used in several studies of multivariate time series data in economy, finance, and business. It is used in numerous studies because of its simplicity. Moreover, the VARMA model can explain the dynamic behavior of the relationship among endogenous and exogenous variables or among endogenous variables. It can also explain the impact of a variable or a set of variables by means of the impulse response function and Granger causality. Furthermore, it can be used to predict and forecast time series data. In this study, we will discuss and develop the best model that describes the relationship between two vectors of time series data export of Coal and data export of Oil in Indonesia over the period 2002-2017. Some models will be applied to the data: VARMA (1,1), VARMA (2,1), VARMA (3,1), and VARMA (4,1). On the basis of the comparison of these models using information criteria AICC, HQC, AIC, and SBC, it was found that the best model is VARMA (2,1) with restriction on some parameters: AR2_1_2 = 0, AR2_2_1 = 0, and MA1_2_1 = 0. The dynamic behavior of the data is studied through Granger causality analysis. The forecasting of the series data is also presented for the next 12 months.

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

“…The data are from the Bureau of Statistics Indonesia (BPS, 2019a;2019b). Before further analysis of the data, first we have to check the assumption of stationarity, some approaches to check this assumption exist: (1) by looking at the behavior of the plot of the data, from where we can analyze and conclude whether the data are stationary or not, and (2) by using analytical approach or statistical test, the ADF test, and other relevant tools (Virginia et al, 2018;Warsono et al, 2019b;2020).…”

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

“…It lies outside the unit circle (Hamilton, 1994;Kirchgassner and Wolters, 2007;Warsono, 2019a;2019b).…”

“…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.…”

Forecasting Stock Prices on the Lq45 Index Using the Varimax Method

“…Various previous studies have discussed VARIMA multivariate time series modeling, including Warsono, et al (2019), which tested forecasting and modeling of coal and oil export data using the VARIMA method, which produced the best multivariate time series model, namely VARIMA (2,0,1). Then, Pratama and Saputro (2018) have analyzed and proven the general equation of the VARIMA multivariate time series method by adding exogenous variables to the model so that the multivariate time series method changes to a Vector Autoregressive Moving Average with Exogenous Variable (VARIMAX).…”