The TSCSREG Procedure

Introductory Example

This 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 kilowatt-hours, and the variable COST is the log of cost in millions of dollars. For more information, see Greene (1990).

   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 two-way random-effects error model where the variance components are estimated by the Fuller-Battese method, because the data are balanced and the parameters are efficiently estimated by using the GLS method. The variance components model used by the Fuller-Battese 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 40.1. 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 40.1: The Variance Components Estimates

The TSCSREG Procedure
Fuller and Battese Variance Components (RanTwo)
 
Dependent Variable: cost

Model Description
Estimation MethodRanTwo
Number of Cross Sections6
Time Series Length4

Fit Statistics
SSE0.3481DFE22
MSE0.0158Root MSE0.1258
R-Square0.8136  

Variance Component Estimates
Variance Component for Cross Sections0.046907
Variance Component for Time Series0.00906
Variance Component for Error0.008749

Hausman Test for Random Effects
DFm ValuePr > m
126.46<.0001

Parameter Estimates
VariableDFEstimateStandard
Error
t ValuePr > |t|Label
Intercept1-2.999920.6478-4.630.0001Intercept
output10.7465960.07629.80<.0001 


Last updated: November 05, 2018