Nonparametric Spline Regression in Inflation Analysis in Indonesia
DOI:
https://doi.org/10.24252/msa.v14i1.64140Keywords:
Inflation, Nonparametric Spline Regression, Consumer Price Index, Interest Rate, Money Supply, GCVAbstract
This study aims to form a nonparametric spline regression model to analyze the factors that influence inflation in Indonesia. The data used are secondary data used are secondary data for the period January 2020-December 2024 from BPS and Bank Indonesia, with inflation variables as responses and CPI, interest rates, exchange rates, and money supply as predictors. The spline model is built with optimal knot points based on minimum Generalized Cross Validation (GCV). The results show that the best model has three knot points with a minimum GCV value of 0.6560 and a coefficient of determination ( of 64.13%). With significant predictor variables on the response variable is the Consumer Price Index variable ( X1 ) , Interest Rate ( X 2 ) and Money Supply ( X4 ) . Thus, nonparametric spline regression is able to capture nonlinear relationships between variables and can be an effective analytical tool in planning inflation control policies.
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