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
| Missing Values |
Embedded missing values in the dependent variable usually cause no problems in UCM modeling. However, no embedded missing values are allowed in the predictor variables. Certain patterns of missing values in the dependent variable can lead to failure of the initialization step of the diffuse Kalman filtering for some models. For example, if in a monthly series all values are missing for a certain month—say, May—then a BSM with monthly seasonality leads to such a situation. However, in this case the initialization step can complete successfully for a nonseasonal model such as local linear model.