Application of Truncated Spline Nonparametric Regression to Modeling the Factors Affecting Economic Growth in South Sulawesi Province

Abstract

Nonparametric regression is a statistical method that does not require a predetermined regression function and is therefore appropriate when the relationship between the response and predictor variables does not follow a specific pattern. One of the most widely used approaches is truncated spline regression, which models the relationship using segmented polynomials connected by knot points, allowing it to flexibly accommodate changes in data patterns. This study aims to model the factors affecting economic growth across regencies and municipalities in South Sulawesi Province in 2024 using truncated spline nonparametric regression. The predictor variables include the percentage of the poor population, the Open Unemployment Rate (OUR), the Labor Force Participation Rate (LFPR), and the Human Development Index (HDI). The best model was selected based on the Generalized Cross Validation (GCV) criterion. The results indicate that the optimal model was obtained with three knot points, yielding a minimum GCV value of 0.85 and a coefficient of determination of 94.32%. All four predictor variables were found to have a statistically significant effect on economic growth in South Sulawesi Province.