Dear all,
for a robustness check in a logit model, I want to test a model which would require the clustering with a low number of clusters (~30). While I found an appropriate package for STATA to calculate standard errors for such cases (boottest), I am currently a bit stuck.
The (simplified) model with a dummy-continuous variable interaction term looks like this:
I want to assess whether the interaction is statistically significant (i.e. whether differences between groups are statistically different, or at least making a statement about the average marginal effects for one of the groups in the interaction terms). As I understood logit models, testing simply the interaction term (i.party#c.vote_share), by difference to OLS, is not sufficient for any statement about significance. Therefore I guess the following command would not produce the desired information
Does anyone have an idea (with boottest or any other package) how to address this issue?
for a robustness check in a logit model, I want to test a model which would require the clustering with a low number of clusters (~30). While I found an appropriate package for STATA to calculate standard errors for such cases (boottest), I am currently a bit stuck.
The (simplified) model with a dummy-continuous variable interaction term looks like this:
Code:
logit funding population tax_revenue i.party##c.vote_share i.region i.year, vce(cluster municipality_ID)
Code:
boottest 1.party#vote_share, cluster(region)
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