Kernel Nonparametric Regression for Forecasting Local Original Income

  • Joji Ardian Pembargi University of Mataram, Indonesia
  • Mustika Hadijati University of Mataram, Indonesia
  • Nurul Fitriyani University of Mataram, Indonesia
Keywords: Bandwidth, Forecasting, Kernel, Modeling, Nonparametric regression


Regional Original Revenue (ROR) is an income collected based on regional regulations under statutory regulations. ROR aims to give authority to Regional Governments to sponsor the implementation of regional autonomy following regional potential. Every year, the Central Lombok Regency government sets ROR targets to assist the government in formulating regional policies. The targets set by the government are sometimes not following their realization. This study aims to determine a model that can be used in forecasting ROR targets. One way to predict the value of ROR is by using a nonparametric regression approach. This approach is flexible since it is not dependent on a particular model. The use of the nonparametric kernel regression method with the Gaussian kernel function obtained a minimum GCV value of 1,769688931 with an optimum bandwidth value of  of 0,212740452 and  of 0,529682589. Modeling with optimum bandwidth produces a coefficient of determination of 87,55%. The best model is used for forecasting and produces a MAPE value of 5,4%. The analysis results show that what influences the value of ROR is ROR receipts in the previous month and the previous 12 months.


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How to Cite
J. Pembargi, M. Hadijati, and N. Fitriyani, “Kernel Nonparametric Regression for Forecasting Local Original Income”, Jurnal Varian, vol. 6, no. 2, pp. 119 - 126, May 2023.