Predicting future inflation in Indonesia using Dynamic Model Averaging (DMA)

Abstract

The features of Indonesia's inflation data, which make it extremely susceptible to shocks like those felt in 2005 and 2008, as well as extensive potential influencing factors, lead to problems in forecasting inflation. These problems include time variation in coefficients, models that can change over time, and many predictors to consider. Dynamic Model Averaging (DMA) solves these problems since it has evolved coefficients and models that change over time. This study uses DMA to predict future inflation by involving eight macroeconomic indicators as exogenous variables. The results of the in-sample analysis show that six predictors are significant in forecasting inflation, with posterior inclusion probability (PIP) being above 40%. Although the remaining predictors have PIP means below 40%, they can still be considered important. The out-of-sample results suggest that DMA performs better than dynamic model selection and models that don’t include exogenous variables, such as autoregressive models. The forecast results indicate a consistent pattern over the 12 months studied. The attempt to control inflation can be achieved by prioritizing the money supply factor, which has the highest PIP value, indicating that it is the most important factor.

Keywords

• Dynamic Model Averaging
• Forecasting
• Inflation