Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
SAS/Ets 9.22 User's Guide 225. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 2232 F Chapter 33 The X11 Procedure markerattrs color red symbol asterisk lineattrs color red legendlabel original series x date y adjusted markers markerattrs color blue symbol circle lineattrs color blue legendlabel adjusted yaxis label Original and Seasonally Adjusted Time Series run Figure 33.3 Plot of Original and Seasonally Adjusted Data X-11-ARIMA An inherent problem with the X-11 method is the revision of the seasonal factor estimates as new data become available. The X-11 method uses a set of centered moving averages to estimate the seasonal components. These moving averages apply symmetric weights to all observations except those at the beginning and end of the series where asymmetric weights have to be applied. These asymmetric weights can cause poor estimates of the seasonal factors which then can cause large revisions when new data become available. X-11-ARIMA F 2233 While large revisions to seasonally adjusted values are not common they can happen. When they do happen it undermines the credibility of the X-11 seasonal adjustment method. A method to address this problem was developed at Statistics Canada Dagum 1980 1982a . This method known as X-11-ARIMA applies an ARIMA model to the original data after adjustments if any to forecast the series one or more years. This extended series is then seasonally adjusted allowing symmetric weights to be applied to the end of the original data. This method was tested against a large number of Canadian economic series and was found to greatly reduce the amount of revisions as new data were added. The X-11-ARIMA method is available in PROC X11 through the use of the ARIMA statement. The ARIMA statement extends the original series either with a user-specified ARIMA model or by an automatic selection process in which the best model from a set of five predefined ARIMA models is used. The following example illustrates the use of the ARIMA statement. The ARIMA statement does not contain a user-specified model so the .
