The AUTOREG Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesAutoregressive Error ModelAlternative Autocorrelation Correction MethodsGARCH ModelsHeteroscedasticity- and Autocorrelation-Consistent Covariance Matrix EstimatorGoodness-of-Fit Measures and Information CriteriaTestingPredicted ValuesOUT= Data SetOUTEST= Data SetPrinted OutputODS Table NamesODS Graphics
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Examples
- References
PROC AUTOREG skips any missing values at the beginning of the data set. If the NOMISS option is specified, the first contiguous set of data with no missing values is used; otherwise, all data with nonmissing values for the independent and dependent variables are used. Note, however, that the observations containing missing values are still needed to maintain the correct spacing in the time series. PROC AUTOREG can generate predicted values when the dependent variable is missing.