The TIMESERIES Procedure |
VAR and CROSSVAR Statements |
The VAR and CROSSVAR statements list the numeric variables in the DATA= data set whose values are to be accumulated to form the time series.
An input data set variable can be specified in only one VAR or CROSSVAR statement. Any number of VAR and CROSSVAR statements can be used. The following options can be used with the VAR and CROSSVAR statements:
specifies how the data set observations are to be accumulated within each time period for the variables listed in the VAR or CROSSVAR statement. If the ACCUMULATE= option is not specified in the VAR or CROSSVAR statement, accumulation is determined by the ACCUMULATE= option of the ID statement. See the ID statement ACCUMULATE= option for more details.
specifies the differencing to be applied to the accumulated time series. The list of differencing orders must be separated by spaces or commas. For example, DIF=(1,3) specifies first then third order differencing. Differencing is applied after time series transformation. The TRANSFORM= option is applied before the DIF= option.
specifies the seasonal differencing to be applied to the accumulated time series. The list of seasonal differencing orders must be separated by spaces or commas. For example, SDIF=(1,3) specifies first then third order seasonal differencing. Differencing is applied after time series transformation. The TRANSFORM= option is applied before the SDIF= option.
specifies how missing values (either actual or accumulated) are to be interpreted in the accumulated time series for variables listed in the VAR or CROSSVAR statement. If the SETMISSING= option is not specified in the VAR or CROSSVAR statement, missing values are set based on the SETMISSING= option of the ID statement. See the ID statement SETMISSING= option for more details.
specifies the time series transformation to be applied to the accumulated time series. The following transformations are provided:
No transformation is applied. This option is the default.
logarithmic transformation
square-root transformation
logistic transformation
Box-Cox transformation with parameter number where the number is between –5 and 5
When the TRANSFORM= option is specified, the time series must be strictly positive.
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