MODELING CROSS SECTION DATA CONTAINS EQUALITY CONSTRAINTS WITH APPLICATION

sometimes we are agree to identify a suitable regression model , for a such kind of data . But the behaviors of data , restrict us to use some procedure to fitting those models , specially if data consists some undesirable behaviors , like some constraints among explanatory variables , and nonlinearity . So the main problems in fitting models , is multicolinearity and also serial auto – correlations among the serial generated residuals When model was fitted , Which they makes the fitted model insignificant . In general the one of mor applicable models for alinear and independent cross-section data , is multiple linear regression , ( ordinary least square estimation , OLS ) , to estimate models parameters . The OLS method is not appropriate for data contains multicolinearity that arise with respect to the constraints , exists on the data . There are several approach of estimation to treat this condition , in this survey , the researcher used , ( restricted least square method , RLS) , to fit model with (equality constraints) in the data under consideration.