SAS/ETS(R) 9.2 User's Guide

The UCM procedure, experimental in SAS System 9, became production in SAS 9.1. You can use this procedure to analyze and forecast equally spaced univariate time series data using unobserved components models (UCM).

The UCMs can be regarded as regression models where, apart from the usual regression variables, the model consists of components such as trend, seasonals, and cycles. In time series literature, UCMs are also referred to as structural models. The different components in a UCM can be modeled separately and are customized to represent salient features of a given time series. The analysis provides separate in-sample and out-of-sample estimates (forecasts) of these component series. In particular, model-based seasonal decomposition and seasonal adjustment of the dependent series is easily available. The distribution of errors in the model is assumed to be Gaussian, and the model parameters are estimated by maximizing the Gaussian likelihood. The UCM procedure can handle missing values in the dependent series.

The domains of applicability of PROC UCM and PROC ARIMA are virtually identical; however, decomposition of a series in features such as trend, seasonals, and cycles is more convenient in PROC UCM. A seasonal decomposition of a time series can also be obtained using other procedures (for example, PROC X12). However, these seasonal decompositions generally do not take into account regression and other effects and are not model based. The seasonal decomposition in PROC UCM is based on a comprehensive model, providing all the advantages of model diagnostics.