Prediction of Inflation Rate in Indonesia Using Local Polynomial Estimator for Time Series Data

Inflation prediction is needed to determine strategies and policies to control a country's economic stability. Inflation is one of the important macroeconomic indicators. Fluctuating inflation rates can disrupt a country's economy, so this is a particular concern. In this paper, inflation prediction was carried out through two approaches: The parametric regression model approach based on the Autoregressive Integrated Moving Average (ARIMA) model and the nonparametric regression approach based on the local polynomial estimator. The model's accuracy is determined based on prediction results using the mean absolute percentage error (MAPE)value. We obtained a MAPE value of 9.0% for the ARIMA model approach and MAPE values of 4.0% and 1.778% for first and second orders, respectively, of local polynomial nonparametric regression model approach. It means that the best model for predicting Indonesia's inflation rate is the second order of local polynomial nonparametric regression model because it has the smallest MAPE value.