Kenneth Train at Berkeley has some Gauss programs to estimate mixed logit models
(also called Random Parameters Logit and Error-Components Logit) using simulated
maximum likelihood.
In a recent paper on mixed logit models (K. Train, "Halton Sequences for Mixed
Logit." ) he describes the mixed logit as follows:
"A mixed logit (MXL) model is essentially a standard logit model with
coefficients that vary in the population. The routine estimates the distribution
of coefficients. MXL does not exhibit independence from irrelevant alternatives
as does standard logit, and allows correlation in unobserved utility over
alternatives and over time.
The utility of an alternative is specified as U=b'x+e, where x is a vector of
observed variables (which vary over alternatives and agents), b is a vector of
unobserved coefficients that vary over agents but not over alternatives
(representing the agent's tastes), and e is an unobserved scalar distributed
extreme value iid over agents and alternatives.
Each coefficient can take any of the following five distributions: (1) Fixed
coefficient: the coefficient is the same for all agents (i.e., a degenerate
distribution). (2) Normally distributed coefficient, with the mean and standard
deviation being estimated. (3) Uniformly distributed coefficients, with the mean
and "spread" being estimated. A uniform distribution with mean b and spread s
has a uniform density between b-s and b+s. (4) Triangularly distributed
coefficients, with the mean and "spread" being estimated. A triangular
distribution with mean b and spread s has zero density below b-s, rises linearly
from b-s to b, decreases linearly from b to b+s, and then is zero again above
b+s. (5) Log-normally distributed coefficient; the coefficient is calculated as
exp(c + s*u) where u is a standard normal deviate and c and s are parameters.
The program estimates c and s. The log-normal distribution with parameters c and
s has median exp(c), mean m=exp[c+((s-squared)/2)], and standard deviation
m*square-root of (exp(s-squared) - 1). "
Scott
----- Original Message -----
>
> Gang Peng wrote:
>
> Are there any command for mixed logit model or do we have to write code on
> our own? Any help? Comment?
>
>
>
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