TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 10

SAS/Ets User's Guide 10. 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 | 82 F Chapter 3 Working with Time Series Data proc forecast data cpicity interval month method expo lead 2 out foreout outfull outresid var cpi id date by city run proc print data foreout obs 6 run The output data set FOREOUT contains many different time series in the single variable CPI. The first few observations of FOREOUT are shown in Figure . BY groups that are identified by the variable CITY contain the result series for the different cities. Within each value of CITY the actual forecast residual and confidence limits series are stored in interleaved form with the observations for the different series identified by the values of _TYPE_. Figure Combined Cross Sections and Interleaved Time Series Data FORECAST Output Data Set with BY Groups Obs city date _TYPE_ _LEAD_ cpi 1 Chicago JAN90 ACTUAL 0 2 Chicago JAN90 FORECAST 0 3 Chicago JAN90 RESIDUAL 0 4 Chicago FEB90 ACTUAL 0 5 Chicago FEB90 FORECAST 0 6 Chicago FEB90 RESIDUAL 0 Output Data Sets of SAS ETS Procedures Some SAS ETS procedures such as PROC FORECAST produce interleaved output data sets and other SAS ETS procedures produce standard form time series data sets. The form a procedure uses depends on whether the procedure is normally used to produce multiple result series for each of many input series in one step as PROC FORECAST does . For example the ARIMA procedure can output actual series forecast series residual series and confidence limit series just as the FORECAST procedure does. The PROC ARIMA output data set uses the standard form because PROC ARIMA is designed for the detailed analysis of one series at a time and so forecasts only one series at a time. The following statements show the use of the ARIMA procedure to produce a forecast of the USCPI data set. Figure shows part of the output data set that is produced by the ARIMA procedure s FORECAST statement. The printed output from PROC ARIMA is not shown. Compare the PROC ARIMA output data .

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