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Journal : MAGISTRA

PERAMALAN INDEKS HARGA KONSUMEN DAN INFLASI INDONESIA DENGAN METODE ARIMA BOX-JENKINS Tripena, Agustini
MAGISTRA Vol 23, No 75 (2011): Magistra Edisi Maret
Publisher : MAGISTRA

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Abstract

In this paper, forecasting the consumer price index data and inflation. The method used Box-JenkinsARIMA. For data that belongs there more than one model that can be used the method of ARIMA (1, 1, 0),ARIMA (0, 1, 1), and ARIMA (1, 1, 1). Thus the value of AIC, ARIMA (1, 1, 1) is the best method for data andconsumer price index inflationThe results showed that the forecast value of the consumer price index based on the model ARIMA (1, 1,1) is for May 2009 was 175.82, in June 2009 was 176.63, and in July 2009 was 177, 65 While the forecastinflation for the month May 2009 is -0.05, June 2009  4105 ??? , and July 2009  4105,5 ??? .Keywords : CPI, inflation, time series, forecasting, ARIMA, AIC
ESTIMATOR DERET FOURIER UNTUK ESTIMASI KURVA REGRESI NONPARAMETRIK BIRESPON Tripena, Agustini
MAGISTRA Vol 25, No 84 (2013): Magistra Edisi Juni
Publisher : MAGISTRA

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Abstract

In the last decade Fourier series estimator in nonparametric regression (one response) a lot of attention from researchers because of its flexibility. In this paper will be developed in Fourier series estimator in nonparametric regression of two responses (Birespon). Data are given in pairs (t1j, y1j), j = 1, 2, ... n1 and (t2j, y2j), j = 1, 2, ... n2,. The relationship between t1j, t2j, y1j and y2j following nonparametric regression model Birespon: y1j = f1 (t1j) +  1j and y2j = f2 (t2j) +  2j Form of regression curves f1 and f2 are unknown and assumed to be contained within the space of continuous functions (0,  ). Random error  1j mutually independent with mean zero and variance 2 1 , and  1j also mutually independent with mean zero and variance 2 2 . Random error  1j and  2j are correlated with the Cor(  1j,  2j) = r. Regression curve f1(t) and f2(t) respectively were approached by a continuous and differentiable function: 1 1( )jd t = 1 1 01 1 1 1 1 cos 2 K j k j k t kt       , And 2 2( )jd t = 2 2 02 2 2 1 1 cos 2 K j k j k t kt       ,  1  ,  2  , 01  , 02  , 1k  , k = 1, 2, ..., n1, 2k  , k = 1, 2, ..., n2 are parameters that are unknown in the Fourier series model. Estimated nonparametric regression curve Birespon f1(t) and f2(t) obtained from the complete optimization Penalized Weighted Least Square (PLST):              0 0 2 2 2221 2 111 2 2 2 1 1 dttfdttfywy ]([ ]([ ))(,,()( n (n "" 21 Completion of this form of optimization PLST Fourier series estimator that can be presented in the form: 1 2 11 1 2 22 ˆ ( )ˆ ( ) ( , ) ˆ ( ) yf t f t B yf t                       . Fourier series estimator in nonparametric regression Birespon is biased to nonparametric regression curve 1 1 2 2 ( ) ( ) ( ) f t f t f t        Although biased, but this estimator is a linear estimator, which is very supportive in building statistical inference for nonparametric regression curve Birespon... Key words: Fourier series estimator, Nonparametric Regression Birespon, Penalized Weighted Least Square