The UCM Procedure

brief information about the input data set, including the data set name and label, and the name of the ID variable specified in the ID statement

summary statistics for the data in the estimation and forecast spans, including the names of the variables in the model, their categorization as dependent or predictor, the index of the beginning and ending observations in the spans, 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 model parameters at the start of the model-fitting stage, including the fixed parameters in the model and the initial estimates of the free parameters in the model

convergence status of the likelihood optimization process if any parameter estimation is done

estimates of the free parameters at the end of the model fitting-stage, including the parameter estimates, their approximate standard errors, t statistics, and the approximate p-value

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

the fit statistics that are based on the raw residuals (observed minus predicted), including the mean squared error (MSE), the root mean squared error (RMSE), the mean absolute percentage error (MAPE), the maximum percentage error (MAXPE), the R square, the adjusted R square, the random walk R square, and Amemiya’s R square

the significance analysis of the components included in the model that is based on the estimation span

brief information about the components included in the model

additive outliers in the series, if any are detected

the multistep series forecasts

post-sample-prediction analysis table that compares the multistep forecasts with the observed series values, if the BACK= option is used in the FORECAST statement