Published: 30 Jun 2013.
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Language: EN
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The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013.

Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.

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