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 2008-2019. 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 < 0.0001 and < 0.0001, respectively, R-squares are 0.8797 and 0.7604, respectively and mean average percentage errors are 12.41 and 6.92, respectively. These models are very reliable, and they can be used to predict (forecast) for the next 12 periods (months).
Highlights
Nowadays, modeling time series data has become an interesting area of research for many scientists
Many studies exist that are related to forecasting: Warsono et al (2019a; 2019b), Virginia et al (2018), who discussed the application of Generalized ARCH (GARCH) model to forecast data and volatility share price of energy, Neslihanoglu et al (2017), who studied modeling for forecasting market model, and application of GARCH model for forecasting volatility model by (Chia et al, 2016)
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 augmented dickey-fuller (ADF) test, and other relevant tools (Virginia et al, 2018; Warsono et al, 2019b; 2020)
Summary
Nowadays, modeling time series data has become an interesting area of research for many scientists. Modeling time series data have been widely used in the fields of economic, business, financial, stock market, social sciences, and many others. Many studies of modeling data time series exist, especially the modeling for forecasting and prediction of future values. Three types of forecasting classification exist based on the periods of times, namely the short term forecast, medium term forecast, and longterm forecast (Montgomery et al, 2008). Many studies exist that are related to forecasting: Warsono et al (2019a; 2019b), Virginia et al (2018), who discussed the application of GARCH model to forecast data and volatility share price of energy, Neslihanoglu et al (2017), who studied modeling for forecasting market model, and application of GARCH model for forecasting volatility model by (Chia et al, 2016)
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