Dear Statalist,
I am interested in estimating the CLOGIT model with complex survey data. I
am estimating a choice model with some variables that are attributes of
the choice X(j) and others that are characteristics of the individual
Z(i).
The model is
Pr(i choses j) = exp[beta*X(j) + gamma(j)*Z(i)] / sum k=1 to M {
exp[beta*X(k) + gamma(k)*Z(i)}
The model can be estimated by converting the data to long format where
each observation becomes M observations with a single instance of the
indicator S=1 for the chosen category. To estimate the gamma(j)
paramaters I need to create interactions of Z(i) with with dummy variables
for the choices.
I am planning to use jackknife deletion of one PSU to estimate the
variance of the parameter vector.
My questions are:
Why is there no svyclogit - is there a theoretical reason that it
is not implemented?
What is the best way to implement a clogit with complex design:
method 1 - write an ML routine and use svy m
method 2 - manually do the jackknife estimation process
a. estimate clogit with pweights where PSU i in
strata h is deleted; save parameter vector Theta_hat(h,i)
b. compute the variance matrix of the parameter
vector as described in the svy manual, page 266
V[Theta_hat] = sum h=1 to L {
[1-f(h)]*m(h) * sum i=1 to n(h) { [Theta_hat(h,i) - Theta_bar(h)] *
[Theta_hat(h,i) - Theta_bar(h)]' } }
Theta_bar(h) = [1/n(h)] * sum i=1 to n(h)
{ Theta_hat(h,i) }
m(h) = [n(h)-1] / n(h)
Thanks for your help!
--Alex Cavallo
Managing Consultant
Navigant Consulting, Inc.
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