Estimating time series semiparametric regression model using local polynomial estimator for predicting inflation rate in Indonesia

Abstract

A model built from a parametric regression model and a nonparametric regression model is called a semiparametric regression (SR) model. The main problem in the SR model is the estimation of the regression function. In this study, we develop the SR model for time series data that is called Time Series Semiparametric Regression (TSSR) model, and discuss estimation of the TSSR model by using local polynomial. Also, we apply it to data of inflation rate (IR) in Indonesia where IR is as response variable, and both IR and money supply in the previous periods are as predictor variables. Next, we compare the results of estimating the IR using the TSSR with the classical method, namely the ARIMA. Also, the TSSR has high accurate criterion for predicting the IR in Indonesia. The results of this study are useful for analyzing Indonesia’s economic growth rate, which is one of the Sustainable Development Goals (SDGs).