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The regression model can be approximated by parametric and nonparametric methods. The parametric regression method generates an excellent regression model when the shape of the curve is known, whereas if the curve shape is random, it can be approximated by using a nonparametric regression method. There is a nonparametric regression method that has been developed such as spline truncated and local polynomials. Spline truncated is segmented pieces regression, while the local polynomial is polynomial models with kernel function as weighted. The most important thing in nonparametric regression modeling is the selection of smoothing parameters. One of the selected parameters of the method is the Generalized Cross Validation (GCV) method. The purpose of this study is to generate the model of Inflation’s sectors in Indonesia using Spline Truncated and local polynomial. These sectors include foodstuffs sector; food, beverages, cigarettes, and tobacco sector; housing, water, electricity, gas, and fuel sector; clothing sector; health sector; education and sports sector; as well as transportation, communication, and financial services group. The results indicated that by modeling the value of the inflation sectors in Indonesia using Spline truncated resulted in average R-square is 68.86% while for local polynomial modeling, the average R-square is 73.73%.

Modelling spline truncated and local polynomial for inflation sectors in Indonesia