The MDC procedure supports various multinomial choice models. However, you can also use PROC MDC to estimate binary choice models such as binary logit and probit because these models are special cases of multinomial models.
Spector and Mazzeo (1980) studied the effectiveness of a new teaching method on students’ performance in an economics course.
They reported grade point average (gpa
), previous knowledge of the material (tuce
), a dummy variable for the new teaching method (psi
), and the final course grade (grade
). A value of 1 is recorded for grade
if a student earned the letter grade “A,” and 0 otherwise.
The binary logit can be estimated using the conditional logit model. In order to use the MDC procedure, the data are converted as follows so that each possible choice corresponds to one observation:
data smdata; input gpa tuce psi grade; datalines; 2.66 20 0 0 2.89 22 0 0 3.28 24 0 0 2.92 12 0 0 ... more lines ...
data smdata1; set smdata; retain id 0; id + 1; /* first choice */ choice1 = 1; choice2 = 0; decision = (grade = 0); gpa_2 = 0; tuce_2 = 0; psi_2 = 0; output; /* second choice */ choice1 = 0; choice2 = 1; decision = (grade = 1); gpa_2 = gpa; tuce_2 = tuce; psi_2 = psi; output; run;
The first 10 observations are displayed in Output 18.1.1. The variables related to grade
=0 are omitted since these are not used for binary choice model estimation.
Output 18.1.1: Converted Binary Data
id  decision  choice2  gpa_2  tuce_2  psi_2 

1  1  0  0.00  0  0 
1  0  1  2.66  20  0 
2  1  0  0.00  0  0 
2  0  1  2.89  22  0 
3  1  0  0.00  0  0 
3  0  1  3.28  24  0 
4  1  0  0.00  0  0 
4  0  1  2.92  12  0 
5  0  0  0.00  0  0 
5  1  1  4.00  21  0 
Consider the choice probability of the conditional logit model for binary choice:

The choice probability of the binary logit model is computed based on normalization. The preceding conditional logit model can be converted as


Therefore, you can interpret the binary choice data as the difference between the first and second choice characteristics. In the following statements, it is assumed that . The binary logit model is estimated and displayed in Output 18.1.2.
/* Conditional Logit */ proc mdc data=smdata1; model decision = choice2 gpa_2 tuce_2 psi_2 / type=clogit nchoice=2 covest=hess; id id; run;
Output 18.1.2: Binary Logit Estimates
Parameter Estimates  

Parameter  DF  Estimate  Standard Error 
t Value  Approx Pr > t 
choice2  1  13.0213  4.9313  2.64  0.0083 
gpa_2  1  2.8261  1.2629  2.24  0.0252 
tuce_2  1  0.0952  0.1416  0.67  0.5014 
psi_2  1  2.3787  1.0646  2.23  0.0255 
Consider the choice probability of the multinomial probit model:

The probabilities of choice of the two alternatives can be written as


where . Assume that and . The binary probit model is estimated and displayed in Output 18.1.3. You do not get the same estimates as that of the usual binary probit model. The probabilities of choice in the binary probit model are


where . However, the multinomial probit model has the error variance if and are independent (). In the following statements, unit variance restrictions are imposed on choices 1 and 2 (). Therefore, the usual binary probit estimates (and standard errors) can be obtained by multiplying the multinomial probit estimates (and standard errors) in Output 18.1.3 by .
/* Multinomial Probit */ proc mdc data=smdata1; model decision = choice2 gpa_2 tuce_2 psi_2 / type=mprobit nchoice=2 covest=hess unitvariance=(1 2); id id; run;
Output 18.1.3: Binary Probit Estimates
Parameter Estimates  

Parameter  DF  Estimate  Standard Error 
t Value  Approx Pr > t 
choice2  1  10.5392  3.5956  2.93  0.0034 
gpa_2  1  2.2992  0.9813  2.34  0.0191 
tuce_2  1  0.0732  0.1186  0.62  0.5375 
psi_2  1  2.0171  0.8415  2.40  0.0165 