SAS/Ets User's Guide 221. 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 | 2192 F Chapter 32 The VARMAX Procedure Table continued ODS Table Name Plot Description Statement ErrorDistribution Distribution of prediction errors MODEL ErrorQQPlot Q-Q plot of prediction errors MODEL ErrorWhiteNoisePlot White noise test of prediction errors MODEL ErrorPlot Prediction errors MODEL ModelPlot Time series and predicted series MODEL AccumulatedIRFPanel Accumulated impulse response function MODEL AccumulatedIRFXPanel Accumulated impulse response of transfer function MODEL OrthogonalIRFPanel Orthogonalized impulse response function MODEL SimpleIRFPanel Simple impulse response function MODEL SimpleIRFXPanel Simple impulse response of transfer function MODEL ModelForecastsPlot Time series and forecasts OUTPUT ForecastsOnlyPlot Forecasts OUTPUT Computational Issues Computational Method The VARMAX procedure uses numerous linear algebra routines and frequently uses the sweep operator Goodnight 1979 and the Cholesky root Golub and Van Loan 1983 . In addition the VARMAX procedure uses the nonlinear optimization NLO subsystem to perform nonlinear optimization tasks for the maximum likelihood estimation. The optimization requires intensive computation. Convergence Problems For some data sets the computation algorithm can fail to converge. Nonconvergence can result from a number of causes including flat or ridged likelihood surfaces and ill-conditioned data. If you experience convergence problems the following points might be helpful Data that contain extreme values can affect results in PROC VARMAX. Rescaling the data can improve stability. Changing the TECH MAXITER and MAXFUNC options in the NLOPTIONS statement can improve the stability of the optimization process. Specifying a different model that might fit the data more closely and might improve convergence. Examples VARMAX Procedure F 2193 Memory Let T be the length of each series k be the number of dependent variables p be the order of autoregressive terms and q be the order of moving-average terms. .
