The X12 Procedure
- Overview
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Getting Started
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Syntax
Functional Summary PROC X12 Statement ADJUST Statement ARIMA Statement AUTOMDL Statement BY Statement CHECK Statement ESTIMATE Statement EVENT Statement FORECAST Statement ID Statement IDENTIFY Statement INPUT Statement OUTLIER Statement OUTPUT Statement REGRESSION Statement TABLES Statement TRANSFORM Statement USERDEFINED Statement VAR Statement X11 Statement
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Details
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Examples
- References
| Missing Values |
PROC X12 can process a series with missing values.
Types of 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 or implicitly by the START= or DATE= option in the PROC X12 statement. By default, leading and trailing missing values are ignored. If you specify the NOTRIMMISS option in the PROC X12 statement, PROC X12 processes leading and trailing missing values according to 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. PROC X12 processes embedded missing values according to the X-12-ARIMA missing value method.
X-12-ARIMA Missing Value Method
When the X-12-ARIMA method encounters a missing value, it inserts an additive outlier for the missing 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.