Hello all,
I am using GEE to estimate my dependent variable. The dependent variable has a lower bound of 0 (its observed value, not censored or truncated) and can take on larger values as well. However, in my dataset, there are a lot of zeros for the dependent variable (about 80% of the time). Would it be acceptable to run a linear GEE model here? (assuming that I probe my results using alternative approaches). From what I understand, GEE is a quasi-likelihood estimator and it has weaker distributional assumptions, so my thoughts were that this would be okay, but I'd be interested in hearing others thoughts. Though to be clear, I am interested in using and defending this approach for my analysis purposes.
Thank you in advance!
Related Posts with GEE and distributional assumptions
Ultimatch - Nearest Neighbor, Radius, Coarsened Exact, Percentile Rank and Mahalanobis Distance Matching in one PackageHi everyone, for all that are interessted in matching (especially by Mahalanobis Distance)... ulti…
Specifying a GARCH(1,1) modelHi, I am trying to find the conditional variance for multiple countries' HP filter ln of GDP using t…
generate dummy variable using strposHi everyone! I have searched this forum and read the official stata help section, but I'm still not…
advice on using duplicates drop commandHello, I would like to make use of "duplicates drop" command to drop out certain rows, For example,…
Issue with an Independent variable consisting of Non Mutually Exclusive Categories, whereby one category perfectly predicts the outcome.Hi Statalist, I'm having a number of problems with my Probit model. I am attempting to model predic…
Subscribe to:
Post Comments (Atom)
0 Response to GEE and distributional assumptions
Post a Comment