TAILIEUCHUNG - SAS/ETS 9.22 User's Guide 85

SAS/Ets User's Guide 85. 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 | 832 F Chapter 15 The FORECAST Procedure is written as follows xt bo bit b2t2 ut ut aiut-1 a2ut-2 ap ut-p ft The autoregressive parameters included in the model for each series are selected by a stepwise regression procedure so that autoregressive parameters are included only at those lags at which they are statistically significant. The stepwise autoregressive method is fully automatic. Unlike the exponential smoothing and Winters methods it does not depend on choosing smoothing weights. However the STEPAR method assumes that the long-term trend is stable that is the time trend regression is fit to the whole series with equal weights for the observations. The stepwise autoregressive model is used when you specify the METHOD STEPAR option or do not specify any METHOD option. To select a constant linear or quadratic trend for the time-trend part of the model use the TREND option. Syntax FORECAST Procedure The following statements are used with PROC FORECAST PROC FORECAST options BY variables ID variables VAR variables Functional Summary Table summarizes the statements and options that control the FORECAST procedure. Table FORECAST Functional Summary Description Statement Option Statements specify model and data set options specify BY-group processing identify observations specify the variables to forecast PROC FORECAST BY ID VAR Input Data Set Options specify the input SAS data set specify frequency of the input time series specify increment between observations PROC FORECAST PROC FORECAST PROC FORECAST DATA INTERVAL INTPER Functional Summary F 833 Description Statement Option specify seasonality PROC FORECAST SEASONS specify number of periods in a season PROC FORECAST SINTPER treat zeros at beginning of series as missing PROC FORECAST ZEROMISS Output Data Set Options specify the number of periods ahead to fore- PROC FORECAST LEAD cast name output data set to contain the forecasts PROC FORECAST OUT write actual values to the OUT data set PROC FORECAST .

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