SAS/Ets User's Guide 129. 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 | 1272 F Chapter 18 The MODEL Procedure starts 1 - d y1 d y2 Resulting log-likelihood function logL 1 2 log 2 log sig1 2 1-d 2 sig2 2 d 2 1 sig1 2 1-d 2 sig2 2 d 2 errormodel starts general logL fit starts method marquardt converge Test for significant differences in the parms test int1 int2 lm test b11 b21 lm test b13 b23 lm test sig1 sig2 lm run Four TEST statements are added to test the hypothesis that the parameters are the same in both regimes. The parameter estimates and ANOVA table from this run are shown in Output . Output Parameter Estimates from the Switching Regression Switching Regression Example The MODEL Procedure Nonlinear Liklhood Summary of Residual Errors DF DF Adj Equation Model Error SSE MSE R-Square R-Sq starts 9 304 Nonlinear Liklhood Parameter Estimates Parameter Estimate Approx Std Err t Value Approx Pr t sigl .0001 sig2 .0001 intl .0001 bll .0001 b13 .0001 int2 .0001 b21 .0001 b23 .0001 P The test results shown in Output suggest that the variance of the housing starts SIG1 and SIG2 are significantly different in the two regimes. The tests also show a significant difference in the AR term on the housing starts. Example Simulating from a Mixture of Distributions F 1273 Output Test Results for Switching Regression Test Results Test Type Statistic Pr ChiSq Label Test0 . int1 int2 Testi . 15636 .0001 b11 b21 Test2 . b13 b23 Test3 . sig1 sig2 Example Simulating from a Mixture of Distributions This example illustrates how to perform a multivariate simulation by using models that have different error distributions. Three models are used. The first model has t distributed errors. The second model .
