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SAS/ETS 9.22 User's Guide 234

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SAS/Ets 9.22 User's Guide 234. 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 | 2322 F Chapter 34 The X12 Procedure PRINT AUTOCHOICEMDL displays the table Models Estimated by Automatic ARIMA Model Selection Procedure. This table summarizes the various models that were considered by the TRAMO automatic model selection method and their measures of fit. PRINT BEST5MODEL displays the table Best Five ARIMA Models Chosen by Automatic Modeling. This table ranks the five best models that were considered by the TRAMO automatic modeling method. BALANCED specifies that the automatic modeling procedure prefer balanced models over unbalanced models. A balanced model is one in which the sum of the AR seasonal AR differencing and seasonal differencing orders equals the sum of the MA and seasonal MA orders. Specifying BALANCED gives the same preference as the TRAMO program. If BALANCED is not specified all models are given equal consideration. HRINITIAL specifies that Hannan-Rissanen estimation be done before exact maximum likelihood estimation to provide initial values. If HRINITIAL is specified then models for which the Hannan-Rissanen estimation has an unacceptable coefficient are rejected. ACCEPTDEFAULT specifies that the default model be chosen if its Ljung-Box Q is acceptable. LJUNGBOXLIMIT va ue specifies acceptance criteria for confidence coefficient of the Ljung-Box Q statistic. If the Ljung-Box Q for a final model is greater than this value the model is rejected the outlier critical value is reduced and outlier identification is redone with the reduced value. See the REDUCECV option for more information. The value specified in the LJUNGBOXLIMIT option must be greater than 0 and less than 1. The default value is 0.95. REDUCECV va ue specifies the percentage that the outlier critical value be reduced when a final model is found to have an unacceptable confidence coefficient for the Ljung-Box Q statistic. This value should be between 0 and 1. The default value is 0.14286. ARMACV va ue specifies the threshold value for the t statistics that are .