The SSM Procedure

brief information about the input data set, including the data set name and label

summary statistics for the response variables in the model, including the names of the variables, the total number of observations and the number of missing observations, the smallest and largest measurements, and the mean and standard deviation

information about the index variable, including the index value of the first and the last observation, the maximum difference between the successive index values, and the categorization of the data into regular, regular with replication, or irregular types

estimates of the regression parameters if the model contains any predictors, including their standard errors, statistics, and -values

convergence status of the likelihood optimization process if any parameters are estimated

estimates of the free parameters at the end of the model-fitting phase, including the parameter estimates and their approximate standard errors

the likelihood-based goodness-of-fit statistics, including the full likelihood, the sum of squares of residuals normalized by their standard errors, and the information criteria: AIC, AICC, HQIC, BIC, and CAIC

summary of most significant additive outliers