When there is only one equation in the estimation, parameters are named in the same way as in other SAS procedures such as REG, PROBIT, and so on. The constant in the regression equation is called Intercept. The coefficients on independent variables are named by the independent variables. The standard deviation of the errors is called _Sigma. If there are BoxCox transformations, the coefficients are named _Lambdai, where i increments from 1, or as specified by the user. The limits for the discrete dependent variable are named _Limiti. If the LIMIT=varying option is specified, then _Limiti starts from 1. If the LIMIT=varying option is not specified, then _Limit1 is set to 0 and the limit parameters start from . If the HETERO statement is included, the coefficients of the independent variables in the hetero equation are called _H.x, where x is the name of the independent variable. If the parameter name includes interaction terms, it needs to be enclosed in quotation marks followed by . The following example restricts the parameter that includes the interaction term to be greater than zero:
proc qlim data=a; model y = x1x2; endogenous y ~ discrete; restrict "x1*x2"N>0; run;
When there are multiple equations in the estimation, the parameters in the main equation are named in the format of y.x, where y is the name of the dependent variable and x is the name of the independent variable. The standard deviation of the errors is called _Sigma.y. The correlation of the errors is called _Rho for bivariate model. For the model with three variables it is _Rho.y1.y2, _Rho.y1.y3, _Rho.y2.y3. The construction of correlation names for multivariate models is analogous. BoxCox parameters are called _Lambdai.y and limit variables are called _Limiti.y. Parameters in the HETERO statement are named as _H.y.x. In the OUTEST= data set, all variables are changed from ’.’ to ’_’.
The following table shows the option in the OUTPUT statement, with the corresponding variable names and their explanation.
Option 
Name 
Explanation 

PREDICTED 
P_y 
Predicted value of y 
RESIDUAL 
RESID_y 
Residual of y, (yPredictedY) 
XBETA 
XBETA_y 
Structure part () of y equation 
ERRSTD 
ERRSTD_y 
Standard deviation of error term 
PROB 
PROB_y 
Probability that y is taking the observed value in this observation (discrete y only) 
PROBALL 
PROBi_y 
Probability that y is taking the ith value (discrete y only) 
MILLS 
MILLS_y 
Inverse Mills ratio for y 
EXPECTED 
EXPCT_y 
Unconditional expected value of y 
CONDITIONAL 
CEXPCT_y 
Conditional expected value of y, condition on the truncation. 
MARGINAL 
MEFF_x 
Marginal effect of x on y () with single equation 
MEFF_y_x 
Marginal effect of x on y () with multiple equations 

MEFF_Pi_x 
Marginal effect of x on y () with single equation and discrete y 

MEFF_Pi_y_x 
Marginal effect of x on y () with multiple equations and discrete y 

TE1 
TE1 
Technical efficiency estimate for each producer proposed by Battese and Coelli (1988) 
TE2 
TE2 
Technical efficiency estimate for each producer proposed by Jondrow et al. (1982) 
If you prefer to name the output variables differently, you can use the RENAME option in the data set. For example, the following statements rename the residual of y as Resid:
proc qlim data=one; model y = x1x10 / censored; output out=outds(rename=(resid_y=resid)) residual; run;