Hi All,

I am using Stata IC 16 to estimate a discrete choice model using the user written command mixlogit written by Arne Hole. Mixlogit generates missing values for cluster-robust standard errors, z values, p values, and the confidence interval when I cluster standard errors on choice set order (t).

The dataset includes choice decisions made by respondents during a single online survey. Respondents were presented with a series of four choice sets and were asked to choose one alternative from each. Each choice set has 3 generic (unlabeled) alternatives (alternative 1, alternative 2, and a status-quo alternative 3). Each alternative is composed of five attributes (price, distance, control, frequency, days). Alternatives 1 and 2 vary freely across choice sets, while the status-quo alternative remaining constant for each individual (varying across individuals).

When I try to cluster standard errors on t (choice set order - 1, 2, 3, or 4), I generate missing values for robust standard errors, z values, p values, and the confidence intervals. I'd like to estimate this model because clustering on t would allow me to control for respondents “learning” in different ways as they progress through each choice set. I've been using the "mixlogit" command successfully thus far, even when I cluster standard errors on pid (unique identifier for each respondent) and gid (unique identifier for each choice set).

Here is my code:

Code:
 global randvars "distance control frequency"
mixlogit y price days if coop_distributor_foodhub=="Yes", rand($randvars) group(gid) id(pid) nrep(1000) cluster (t)
Here is the estimate table this code generates:

Array


Any feedback would be greatly appreciated!

Sincerely,

Julia Jones
Department of Natural Resources and the Environment
University of New Hampshire