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The TSCSREG Procedure

Unbalanced Data

In the case of fixed effects and random effects models, the TSCSREG procedure is capable of processing data with different numbers of time series observations across different cross sections. You must specify the ID statement to estimate models using unbalanced data. The missing time series observations are recognized by the absence of time series id variable values in some of the cross sections in the input data set. Moreover, if an observation with a particular time series id value and cross-sectional id value is present in the input data set, but one or more of the model variables are missing, that time series point is treated as missing for that cross section.

Also, when PROC TSCSREG is processing balanced data, you now need to specify only the CS= parameter if you do not specify an ID statement. The TS= parameter is not required, since it can be inferred from the number of observations if the data is balanced.

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