tailieunhanh - SAS/ETS 9.22 User's Guide 31

SAS/Ets User's Guide 31. 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 | 292 F Chapter 7 The ARIMA Procedure Output Plot of the Forecast for the Original Series Example Model for Series J Data from Box and Jenkins This example uses the Series J data from Box and Jenkins 1976 . First the input series X is modeled with a univariate ARMA model. Next the dependent series Y is cross-correlated with the input series. Since a model has been fit to X both Y and X are prewhitened by this model before the sample cross-correlations are computed. Next a transfer function model is fit with no structure on the noise term. The residuals from this model are analyzed then the full model transfer function and noise is fit to the data. The following statements read Input Gas Rate and Output CO2 from a gas furnace. Data values are not shown. The full example including data is in the SAS ETS sample library. title1 Gas Furnace Data title2 Box and Jenkins Series J data seriesj input x y @@ label x Input Gas Rate y Output CO2 Example Model for Series J Data from Box and Jenkins F 293 datalines . more lines . The following statements produce Output through Output ods graphics on proc arima data seriesj ---Look at the input process------------------------- identify var x run ---Fit a model for the input------------------------- estimate p 3 plot run --- Crosscorrelation of prewhitened series ---------- identify var y crosscorr x nlag 12 run - Fit a simple transfer function - look at residuals - estimate input 3 1 2 1 x run --- Final Model - look at residuals ------ estimate p 2 input 3 1 2 1 x run quit The results of the first IDENTIFY statement for the input series X are shown in Output . The correlation analysis suggests an AR 3 model. Output IDENTIFY Statement Results for X Gas Furnace Data Box and Jenkins Series J The ARIMA Procedure Name of Variable x Mean of Working Series Standard Deviation Number of Observations 296 294 F Chapter 7 The ARIMA Procedure Output IDENTIFY Statement Results for X .