tailieunhanh - SAS/ETS 9.22 User's Guide 121

SAS/Ets User's Guide 121. 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 | 1192 F Chapter 18 The MODEL Procedure If k is the number of general form equations then k derivatives are required. The convergence properties of the Jacobi and Seidel solution methods remain significantly poorer than the default Newton s method. Comparison of Methods Newton s method is the default and should work better than the others for most small- to mediumsized models. The Seidel method is always faster than the Jacobi for recursive models with equations in recursive order. For very large models and some highly nonlinear smaller models the Jacobi or Seidel methods can sometimes be faster. Newton s method uses more memory than the Jacobi or Seidel methods. Both the Newton s method and the Jacobi method are order-invariant in the sense that the order in which equations are specified in the model program has no effect on the operation of the iterative solution process. In order-invariant methods the values of the solution variables are fixed for the entire execution of the model program. Assignments to model variables are automatically changed to assignments to corresponding equation variables. Only after the model program has completed execution are the results used to compute the new solution values for the next iteration. Troubleshooting Problems In solving a simultaneous nonlinear dynamic model you might encounter some of the following problems. Missing Values For SOLVE tasks there can be no missing parameter values. Missing right-hand-side variables result in missing left-hand-side variables for that observation. Unstable Solutions A solution might exist but be unstable. An unstable system can cause the Jacobi and Seidel methods to diverge. Explosive Dynamic Systems A model might have well-behaved solutions at each observation but be dynamically unstable. The solution might oscillate wildly or grow rapidly with time. Propagation of Errors During the solution process solution variables can take on values that cause computational errors. For example a .