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SAS/Ets 9.22 User's Guide 99. 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 | 972 F Chapter 17 The MDC Procedure data trichoice array error ndim e1-e3 array vtemp ndim _temporary_ array lm 6 _temporary_ 1.4142136 0.4242641 0.9055385 001 retain nseed 345678 do id 1 to Snobs index 0 generate independent normal variate do i 1 to ndim index of diagonal element vtemp i rannor nseed end get multivariate normal variate index 0 do i 1 to ndim error i 0 do j 1 to i error i error i lm index j vtemp j end index index i end x1 1.0 2.0 ranuni nseed x2 1.2 2.0 ranuni nseed x3 1.5 1.2 ranuni nseed util1 2.0 x1 e1 util2 2.0 x2 e2 util3 2.0 x3 e3 do i 1 to ndim vtemp i 0 end if util1 util2 util1 util3 then vtemp 1 1 else if util2 util1 util2 util3 then vtemp 2 1 else if util3 util1 util3 util2 then vtemp 3 1 else continue -- first choice -- x x1 mode 1 decision vtemp 1 output -- second choice -- x x2 mode 2 decision vtemp 2 output -- third choice -- x x3 mode 3 decision vtemp 3 output end Example 17.3 Correlated Choice Modeling F 973 run First the multinomial probit model is estimated see the following statements . Results show that the standard deviation correlation and slope estimates are close to the parameter values. Note that P12 CT12 6 y 0.42 o1 F2 1.41 o2 VT 1 and the parameter value for y CT2 CT2 Vi2 .1 the variable x is 2.0. See Output 17.3.1. Trinomial Probit proc mdc data trichoice randnum halton nsimul 100 model decision x type mprobit choice mode 123 covest op optmethod qn id id run Output 17.3.1 Trinomial Probit Model Estimation The MDC Procedure Multinomial Probit Estimates Parameter Estimates Parameter DF Estimate Standard Error t Value Approx Pr t x 1 1.7987 0.1202 14.97 .0001 STD_1 1 1.2824 0.1468 8.74 .0001 RHO_21 1 0.4233 0.1041 4.06 .0001 Figure 17.29 shows a two-level decision tree. Figure 17.29 Nested Tree Structure 974 F Chapter 17 The MDC Procedure The following statements estimate the nested model shown in Figure 17.29 Two-Level Nested Logit proc mdc data trichoice model decision x type nlogit choice mode 123 covest op optmethod qn
