Predicting Inflation in Indonesia Using Bi- predictors Semiparametric Model Based on Local Polynomial Estimator - Aziz, Abdul; Chamidah, Nur; Saifudin, Toha (Proceedings of the International Conference on Green Technology; Vol 14 No 1 (2024): Proceeding of 14th International Conference on Green Technology, 2025)
Inflation is a condition of general and continuous increases in the prices of goods and services over a certain period of time. High and fluctuating inflation rates are a sign of economic instability. This fluctuating nature is due to factors that influence it, causing the relationship patterns in the data to not form a certain pattern and are also used for predictions. This research applies a semiparametric regression model, which combines parametric and nonparametric model, using a local polynomial estimator for inflation data with 2 factors that influence inflation, namely the Bank Indonesia (BI) interest rate in one previous month and the change rate of Money Supply (JUB) in one previous month. The local polynomial method estimates nonparametric functions by considering the local polynomial order and the optimum bandwidth value based on the lowest GCV value. In this research, a semiparametric regression model was obtained with an optimum bandwidth value of order 1, with high accuracy (MAPE 9.61%). The inflation predictions for September 2024, where the value is not yet known, with the BI interset rate and the change rate of JUB values in one previous month, using the model resulted in this research, the predicted value is 2.12%.