PROC X12 can process a series with missing values. Missing values in a series are considered to be one of two types:

• One type of missing value is a leading or trailing missing value, which occurs before the first nonmissing value or after the last nonmissing value, respectively, in the span of a series. The span of a series can be determined either explicitly by the SPAN= option of the PROC X12 statement or implicitly by the START= or DATE= options. By default, leading and trailing missing values are ignored. The NOTRIMMISS option of the PROC X12 statement causes leading and trailing missing values to also be processed using the X-12-ARIMA missing value method.

• The second type of missing value is an embedded missing value. These missing values occur between the first nonmissing value and the last nonmissing value in the span of the series. Embedded missing values are processed using X-12-ARIMA’s missing value method described below.

When the X-12-ARIMA method encounters a missing value, it inserts an additive outlier for that observation into the set of regression variables for the model of the series and then replaces the missing observation with a value large enough to be considered an outlier during model estimation. After the regARIMA model is estimated, the X-12-ARIMA method adjusts the original series by using factors generated from these missing value outlier regressors. The adjusted values are estimates of the missing values, and the adjusted series is displayed in Table MV1.