names the output SAS data set containing the predicted and transformed values. If the OUT= option is not specified, the new data set is named according to the DATAn convention.

sets the confidence limit size for the estimates of future values of the response time series. The ALPHACLI= value must be between 0 and 1. The resulting confidence interval has 1-number confidence. The default is ALPHACLI=.05, corresponding to a 95% confidence interval.

sets the confidence limit size for the estimates of the structural or regression part of the model. The ALPHACLI= value must be between 0 and 1. The resulting confidence interval has 1-number confidence. The default is ALPHACLM=.05, corresponding to a 95% confidence interval.

specifies the significance level for the upper and lower bounds of the CUSUM and CUSUMSQ statistics output by the CUSUMLB=, CUSUMUB=, CUSUMSQLB=, and CUSUMSQUB= options. The significance level specified by the ALPHACSM= option can be .01, .05, or .10. Other values are not supported.

The following options are of the form KEYWORD=name, where KEYWORD specifies the statistic to include in the output data set and name gives the name of the variable in the OUT= data set containing the statistic.

specifies the name of a variable to contain the values of the Theil’s BLUS residuals. Refer to Theil (1971) for more information on BLUS residuals.

writes to the output data set the value of the error variance from the heteroscedasticity model specified by the HETERO statement or the value of the conditional error variance by the GARCH= option in the MODEL statement.

writes the conditional prediction error variance to the output data set. The value of conditional prediction error variance is equal to that of the conditional error variance when there are no autoregressive parameters. For the exponential GARCH model, conditional prediction error variance cannot be calculated. See the section Predicted Values later in this chapter for details.

writes the transformed intercept to the output data set. The details of the transformation are described in Computational Methods later in this chapter.

specifies the name of a variable to contain the CUSUM statistics.

specifies the name of a variable to contain the CUSUMSQ statistics.

specifies the name of a variable to contain the upper confidence bound for the CUSUM statistic.

specifies the name of a variable to contain the lower confidence bound for the CUSUM statistic.

specifies the name of a variable to contain the upper confidence bound for the CUSUMSQ statistic.

specifies the name of a variable to contain the lower confidence bound for the CUSUMSQ statistic.

writes the lower confidence limit for the predicted value (specified in the PREDICTED= option) to the output data set. The size of the confidence interval is set by the ALPHACLI= option. See the section Predicted Values later in this chapter for details.

writes the lower confidence limit for the structural predicted value (specified in the PREDICTEDM= option) to the output data set under the name given. The size of the confidence interval is set by the ALPHACLM= option.

writes the predicted values to the output data set. These values are formed from both the structural and autoregressive parts of the model. See the section Predicted Values later in this chapter for details.

writes the structural predicted values to the output data set. These values are formed from only the structural part of the model. See the section Predicted Values later in this chapter for details.

specifies the name of a variable to contain the part of the predictive error variance () that is used to compute the recursive residuals.

specifies the name of a variable to contain recursive residuals. The recursive residuals are used to compute the CUSUM and CUSUMSQ statistics.

writes the residuals from the predicted values based on both the structural and time series parts of the model to the output data set.

writes the residuals from the structural prediction to the output data set.

transforms the specified variables from the input data set by the autoregressive model and writes the transformed variables to the output data set. The details of the transformation are described in Computational Methods later in this chapter. If you need to reproduce the data suitable for reestimation, you must also transform an intercept variable. To do this, transform a variable that is all 1s or use the CONSTANT= option.

writes the upper confidence limit for the predicted value (specified in the PREDICTED= option) to the output data set. The size of the confidence interval is set by the ALPHACLI= option. See the section Predicted Values later in this chapter for details.

writes the upper confidence limit for the structural predicted value (specified in the PREDICTEDM= option) to the output data set. The size of the confidence interval is set by the ALPHACLM= option.