The following example uses the cost function data from Greene (1990) to estimate the variance components model. The variable OUTPUT is the log of output in millions of kilowatthours, and COST is the log of cost in millions of dollars. Refer to Greene (1990) for details.
title1; data greene; input firm year output cost @@; df1 = firm = 1; df2 = firm = 2; df3 = firm = 3; df4 = firm = 4; df5 = firm = 5; d60 = year = 1960; d65 = year = 1965; d70 = year = 1970; datalines; 1 1955 5.36598 1.14867 1 1960 6.03787 1.45185 ... more lines ...
Usually you cannot explicitly specify all the explanatory variables that affect the dependent variable. The omitted or unobservable variables are summarized in the error disturbances. The TSCSREG procedure used with the RANTWO option specifies the twoway randomeffects error model where the variance components are estimated by the FullerBattese method, because the data are balanced and the parameters are efficiently estimated by using the GLS method. The variance components model used by the FullerBattese method is

The following statements fit this model.
proc sort data=greene; by firm year; run; proc tscsreg data=greene; model cost = output / rantwo; id firm year; run;
The TSCSREG procedure output is shown in Figure 34.1. A model description is printed first; it reports the estimation method used and the number of cross sections and time periods. The variance components estimates are printed next. Finally, the table of regression parameter estimates shows the estimates, standard errors, and t tests.
Figure 34.1: The Variance Components Estimates
Model Description  

Estimation Method  RanTwo 
Number of Cross Sections  6 
Time Series Length  4 
Fit Statistics  

SSE  0.3481  DFE  22 
MSE  0.0158  Root MSE  0.1258 
RSquare  0.8136 
Variance Component Estimates  

Variance Component for Cross Sections  0.046907 
Variance Component for Time Series  0.00906 
Variance Component for Error  0.008749 
Hausman Test for Random Effects  

DF  m Value  Pr > m 
1  26.46  <.0001 
Parameter Estimates  

Variable  DF  Estimate  Standard Error  t Value  Pr > t 
Intercept  1  2.99992  0.6478  4.63  0.0001 
output  1  0.746596  0.0762  9.80  <.0001 