摘要：Regression method is a statistical method for modelling dependent variable with independent variable. Nonparametric regression is an approach to regression analysis that is suitable for data that have an unknown curve shape. Modelling by using nonparametric regression method does not require any assumptions. Spline and Fourier methods are some of the estimators in nonparametric regression. The spline method requires optimal knots to obtain the best model. The most commonly used method to determine the optimal knots is Generalized Cross Validation (GCV). The Fourier method is a method based on the cosine and sinus series. The Fourier method is particularly suitable for data that experience repetitive patterns. This study modeled the Inflation rate in Indonesia from January 2007 to August 2017. The dependent variable is inflation rate, while the independent variable is time. From the result, linear spline regression estimation with three knots that generates R square of 60%. The best Fourier model is Fourier with K = 100 that generates R square of 80.12%. The best Spline model is with 9 knots generates R square of 87.65%, so, for inflation modelling in Indonesia, the spline regression model generates a simpler model with better R-square than Fourier regression.
其他摘要：Regression method is a statistical method for modelling dependent variable with independent variable. Nonparametric regression is an approach to regression analysis that is suitable for data that have an unknown curve shape. Modelling by using nonparametric regression method does not require any assumptions. Spline and Fourier methods are some of the estimators in nonparametric regression. The spline method requires optimal knots to obtain the best model. The most commonly used method to determine the optimal knots is Generalized Cross Validation (GCV). The Fourier method is a method based on the cosine and sinus series. The Fourier method is particularly suitable for data that experience repetitive patterns. This study modeled the Inflation rate in Indonesia from January 2007 to August 2017. The dependent variable is inflation rate, while the independent variable is time. From the result, linear spline regression estimation with three knots that generates R square of 60%. The best Fourier model is Fourier with K = 100 that generates R square of 80.12%. The best Spline model is with 9 knots generates R square of 87.65%, so, for inflation modelling in Indonesia, the spline regression model generates a simpler model with better R-square than Fourier regression.
