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SAS/Ets 9.22 User's Guide 130. 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 | 1282 F Chapter 18 The MODEL Procedure do i 1 to 500 x rannor 1013 Y 2 1.5 x 1.5 rannor 1013 output end run proc model data regdata parms a b s instrument x ysim a b x s rannor 8003 y ysim eq.ysq y y - ysim ysim fit y ysq gmm ndraw bound s 0 run Alternatively the MOMENT statement can be used to specify the moments using the following syntax proc model data regdata parms a b s instrument x ysim a b x s rannor 8003 y ysim moment y 2 fit y gmm ndraw bound s 0 run The output of the MODEL procedure is shown in Output 18.15.1 Output 18.15.1 PROC MODEL Output Simple regression model The MODEL Procedure Model Summary Model Variables 1 Parameters 3 Equations Number of Statements 2 4 Model Variables Y Parameters a b s Equations ysq Y Example 18.16 Simulated Method of Moments AR 1 Process F 1283 Output 18.15.1 continued The 2 Equations to Estimate Y F a 1 b x s ysq F a b s Instruments 1x Nonlinear GMM Parameter Estimates Approx Approx Parameter Estimate Std Err t Value Pr t a 2.065983 0.0657 31.45 .0001 b 1.511075 0.0565 26.73 .0001 s 1.483358 0.0498 29.78 .0001 Example 18.16 Simulated Method of Moments AR 1 Process This example illustrates how to use SMM to estimate an AR 1 regression model for the following process yt a bxt ut ut aut-i et et iid N.0 s2 In the following SAS statements ysim is simulated by using this model and the endogenous variable y is set to be equal to ysim. The MOMENT statement creates two more moments for the estimation. One is the second moment and the other is the first-order autocovariance. The NPREOBS 10 option instructs PROC MODEL to run the simulation 10 times before ysim is compared to the first observation of y. Because the initial zlag u is zero the first ysim is a b x s rannor 8003 . Without the NPREOBS option this ysim is matched with the first observation of y. With NPREOBS this ysim and the next nine ysim are thrown away and the moment match starts with the eleventh ysim with the first observation of y. This way the initial values do not .
