FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH METHOD

Abstract

Consumer Price Index (CPI) is an index number that shows the level of prices of goods and services purchased by consumers in a certain period. Forecasting related to CPI data needs to be done to describe the price level of goods and services purchased by the public. There are several methods in statistics that can be used for forecasting, one of which is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. GARCH has advantages compared to other forecasting methods, namely that it can apply to data that has high volatility. Volatility occurs if the data variance is not constant and will certainly cause the data to be non-stationary so that it does not meet the assumptions in the time series analysis. The purpose of this study was to determine the best model of the GARCH method and to find out the prediction results of the CPI for the future period. Based on the results of the analysis, the best models used are GARCH (1,0). And the CPI value in January 2022 was 112,1116.