ANALYZING THE CONSUMER’S RICE PRICE USING MULTIPLE LINEAR REGRESSION AND X-12 ARIMA - Dian Kusumaningrum,; Asep Saefuddin; Anang Kurnia (STATISTIKA: Forum Teori dan Aplikasi Statistika, 2004)
Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. Onthe other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to therice price, as well as forecasting the consumer’s rice price are needed in order to maintain the stabilization of rice price.The second objective is to find key factors which influence the rice price by using multiple linear regression models. Theparameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which arethe consumer’s rice price at the previous period, rice production at the current and previous period, farmer’s GKP price,realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domesticstock increase whereas total rice import and the consumer’s rice price at the previous period have negative influencestowards the rice price. In this model rice production at the current and previous period have positive signs, contradictory tothe microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop.That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but inIndonesia, rice is affected by the government’s policy and the rice productivity is left behind by the demand. Forecastingthe consumer’s rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12ARIMA, Winter’s Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer’srice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. Thisresult is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal andtrading day factors which usually causes fluctuations in an economic time series data. Besides that, the X-12 ARIMAmethod also enhances the lack of other forecasting techniques used in this research to add regression effects. TheregARIMA makes it possible to add the user defined parameters, in this case the length of month parameter. The length ofmonth parameter rescales the monthly observation by a weight corresponding to the month relative length with respect tothe average length. The seasonal adjusted data from the original time series data is aimed to simplify the data withoutloosing important information.