tailieunhanh - SAS/ETS 9.22 User's Guide 29

SAS/Ets User's Guide 29. 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 | 272 F Chapter 7 The ARIMA Procedure OUTSTAT Data Set PROC ARIMA writes the diagnostic statistics for a model to an output data set when the OUTSTAT option is specified in the ESTIMATE statement. The OUTSTAT data set contains the following the BY variables. _MODLABEL_ a character variable that contains the model label if it is provided by using the label option in the ESTIMATE statement otherwise this variable is not created . _TYPE_ a character variable that contains the estimation method used. _TYPE_ can have the value CLS ULS or ML. _STAT_ a character variable that contains the name of the statistic given by the _VALUE_ variable in this observation. _STAT_ takes on the values AIC SBC LOGLIK SSE NUMRESID NPARMS NDIFS ERRORVAR MU CONV and NITER. _VALUE_ a numeric variable that contains the value of the statistic named by the _STAT_ variable. The observations contained in the OUTSTAT data set are identified by the _STAT_ variable. A description of the values of the _STAT_ variable follows AIC Akaike s information criterion SBC Schwarz s Bayesian criterion LOGLIK the log-likelihood if METHOD ML or METHOD ULS is specified SSE the sum of the squared residuals NUMRESID the number of residuals NPARMS the number of parameters in the model NDIFS the sum of the differencing lags employed for the response variable ERRORVAR the estimate of the innovation variance MU the estimate of the mean term CONV tells if the estimation converged. The value of 0 signifies that estimation converged. Nonzero values reflect convergence problems. NITER the number of iterations Remark. CONV takes an integer value that corresponds to the error condition of the parameter estimation process. The value of 0 signifies that estimation process has converged. The higher values signify convergence problems of increasing severity. Specifically CONV 0 indicates that the estimation process has converged. CONV 1 or 2 indicates that the estimation process has run into numerical problems such as encountering