I am struggling interpreting sigma in a random effect panel probit regression and why is this term strongly significant. I understand all the others results from the regressions.
I used the following fit call :
Model<-pglm(Variable_to_explain ~ fv + context + male + context*male, model=("random"), effect=("twoways"), index=c("indiv", "period"), family=binomial(link="probit"), data=data)
Any help will be very appreciated.