TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 227

SAS/Ets User's Guide 227. 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 | 2252 F Chapter 33 The X11 Procedure While various methods of extending a series have been proposed the most important method to date has been the X-11-ARIMA method developed at Statistics Canada. This method uses Box-Jenkins ARIMA models to extend the series. The Time Series Research and Analysis Division of Statistics Canada investigated 174 Canadian economic series and found five ARIMA models out of twelve that fit the majority of series well and reduced revisions for the most recent months. References that give details of various aspects of the X-11-ARIMA methodology include Dagum 1980 1982a c 1983 1988 Laniel 1985 Lothian and Morry 1978a and Huot et al. 1986 . Differences between X11ARIMA 88 and PROC X11 The original implementation of the X-11-ARIMA method was by Statistics Canada in 1980 Dagum 1980 with later changes and enhancements made in 1988 Dagum 1988 . The calculations performed by PROC X11 differ from those in X11ARIMA 88 which will result in differences in the final component estimates provided by these implementations. There are three areas where Statistics Canada made changes to the original X-11 seasonal adjustment method in developing X11ARIMA 80 Monsell 1984 . These are a selection of extreme values b replacement of extreme values and c generation of seasonal and trend cycle weights. These changes have not been implemented in the current version of PROC X11. Thus the procedure produces results identical to those from previous versions of PROC X11 in the absence of an ARIMA statement. Additional differences can result from the ARIMA estimation. X11ARIMA 88 uses conditional least squares CLS while CLS unconditional least squares ULS and maximum likelihood ML are all available in PROC X11 by using the METHOD option in the ARIMA statement. Generally parameters estimates will differ for the different methods. Implementation of the X-11 Seasonal Adjustment Method The following steps describe the analysis of a monthly time series using multiplicative .

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• Hướng dẫn sử dụng SAS ETS
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