Thanks Scott. I know that paper, but wondered if they have implemented the
procedure in Stata. It seems not yet.
----- Original Message -----
From: "Scott Merryman" <[email protected]>
To: <[email protected]>
Sent: Monday, January 26, 2004 7:33 PM
Subject: Re: st: how to estimate mixed logit in Stata?
> 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?
> >
> >
> >
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
