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The TSCSREG Procedure |
The TSCSREG (Time Series Cross Section Regression) procedure analyzes a class of linear econometric models that commonly arise when time series and cross-sectional data are combined. The TSCSREG procedure deals with panel data sets that consist of time series observations on each of several cross-sectional units. Such models can be viewed as two-way designs with covariates
where N is the number of cross sections, T is the length of the time series for each cross section, and K is the number of exogenous or independent variables.
The performance of any estimation procedure for the model regression parameters depends on the statistical characteristics of the error components in the model. The TSCSREG procedure estimates the regression parameters in the preceding model under several common error structures. The error structures and the corresponding methods the TSCSREG procedure uses to analyze them are as follows:
The TSCSREG procedure analyzes panel data sets that consist of multiple time series observations on each of several individuals or cross-sectional units. The input data set must be in time series cross-sectional form. See Chapter 2, "Working with Time Series Data," for a discussion of how time series related by a cross-sectional dimension are stored in SAS data sets. The TSCSREG procedure requires that the time series for each cross section have the same number of observations and cover the same time range.
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