The UCM Procedure
- Overview
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Getting Started
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Syntax
Functional Summary PROC UCM Statement AUTOREG Statement BLOCKSEASON Statement BY Statement CYCLE Statement DEPLAG Statement ESTIMATE Statement FORECAST Statement ID Statement IRREGULAR Statement LEVEL Statement MODEL Statement NLOPTIONS Statement OUTLIER Statement RANDOMREG Statement SEASON Statement SLOPE Statement SPLINEREG Statement SPLINESEASON Statement
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Details
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Examples
- References
| Displayed Output |
The default printed output produced by the UCM procedure is described in the following list:
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