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Deiby Tineke Salaki
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

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Application of Nonparametric Spline Regression and Fourier Series to Model Gross Regional Domestic Product in Indonesia Marklif Esriy Mocodompis; Deiby Tineke Salaki; Djoni Hatidja
Jurnal Ilmiah Sains Volume 23 Nomor 2, Oktober 2023
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jis.v23i2.48868

Abstract

The purpose of this study was to model the Gross Regional Domestic Product (GRDP) in Indonesia using nonparametric spline regression and Fourier series. The data used includes the GRDP of 34 provinces in Indonesia, with predictor variables such as labor force participation rate, foreign direct investment, local revenue, minimum provincial wage, and human development index. In the nonparametric spline regression, the determination of the optimal knot points is performed with one to three knots, and the optimal knot points are obtained by minimizing the Generalized Cross Validation (GCV) value, resulting in three optimal knot points. In the nonparametric Fourier series regression, calculations are performed for one to three oscillation points, and the optimal oscillation points are obtained based on the minimum GCV value, resulting in three optimal oscillation points. Therefore, the best nonparametric spline regression model utilizes three knot points, while the Fourier series model utilizes three oscillation points for modeling the GRDP in Indonesia. Keywords: Fourier Series; nonparametric regression; oscillation point; spline
Co-Authors Djoni Hatidja Marklif Esriy Mocodompis