Application of the ARIMA-GARCH Model for Forecasting Indonesia's Monthly Inflation Rate

Inflation is one of the important aspects that is used as a benchmark to see economic growth and economic conditions in each country. Inflation has resulted in increasing public expenditure in meeting basic needs. Inflation must be controlled to maintain the economic stability of a country, including Indonesia. Therefore, there is a need for a model that can forecast the inflation rate in Indonesia. The aim of this research is to create a model that can predict future inflation levels so that it can help the government in determining policies related to controlling inflation in Indonesia. The data used is monthly inflation data in Indonesia for 19 years from March 2007- October 2023 in percentage form. The forecasting model used in this study is the ARIMA-GARCH model. The ARIMA model is a time series model used to forecast future data based on past data. While GARCH is a time series model used to overcome heteroscedasticity in the ARIMA model. Inflation data will be modeled using the ARIMA model and then continued by modeling the residuals using the GARCH model if heteroscedasticity occurs in the ARIMA model residuals. Based on data analysis that has been done, the best model for inflation forecasting cases in Indonesia is the ARIMA (2,0,2) - GARCH (0,1) model with a MAPE value of 17.78%.