One way of modeling unobserved heterogeneity across individuals in their sensitivity to observed exogenous variables is to use the mixed logit model with a random parameters or random coefficients specification. The probability of choosing alternative is written as

where is a vector of coefficients that varies across individuals and is a vector of exogenous attributes.
For example, you can specify the distribution of the parameter to be the normal distribution.
The mixed logit model uses a Monte Carlo simulation method to estimate the probabilities of choice. There are two simulation methods available. If the RANDNUM=PSEUDO option is specified in the MODEL statement, pseudorandom numbers are generated; if the RANDNUM=HALTON option is specified, Halton quasirandom sequences are used. The default value is RANDNUM=HALTON.
You can estimate the model with normally distributed random coefficients of ttime
with the following SAS statements:
/* mixed logit estimation */ proc mdc data=newdata type=mixedlogit; model decision = ttime / nchoice=3 mixed=(normalparm=ttime); id pid; run;
Let and be mean and scale parameters, respectively, for the random coefficient, . The relevant utility function is

where ( and are fixed mean and scale parameters, respectively). The stochastic component, , is assumed to be standard normal since the NORMALPARM= option is given. Alternatively, the UNIFORMPARM= or LOGNORMALPARM= option can be specified. The LOGNORMALPARM= option is useful when nonnegative parameters are being estimated. The NORMALPARM=, UNIFORMPARM=, and LOGNORMALPARM= variables must be included in the righthand side of the MODEL statement. See the section Mixed Logit Model for more details. To estimate a mixed logit model by using the transportation mode choice data, the MDC procedure requires the MIXED= option for random components. Results of the mixed logit estimation are displayed in Figure 18.21.
Figure 18.21: Mixed Logit Model Parameter Estimates
Parameter Estimates  

Parameter  DF  Estimate  Standard Error 
t Value  Approx Pr > t 
ttime_M  1  0.5342  0.2184  2.45  0.0144 
ttime_S  1  0.2843  0.1911  1.49  0.1368 
Note that the parameter ttime_M
corresponds to the constant mean parameter and the parameter ttime_S
corresponds to the constant scale parameter of the random coefficient .