Dear Stata Users,

I am using copulas as a tool to better understand the nuanced dependency amongst education outcomes, e.g. dropout and approved.

I have been able to use bicop command to run all the estimations and define the best model.

Code:
local maxll=minfloat()

* 

* Mixture <- specifies the marginal distribution of each residual
   * none specifies each marginal distribution as an N(0, 1) form;

foreach cop in gaussian frank clayton gumbel joe indep {
 local xvars boy nowi urb age govaid pc element sroom lib sci sports tage tagesd stu_staff_ratio 
 bicop status edrop `xvars', copula(`cop') mixture(none)
 estimates store `cop'
 if e(ll)> `max11' e(converged) {
 local `max11'=e(11)
 local bestcop="`cop'"
 matrix bestb=e(b)
 }
}

* Estimation summary statistics 
estimates stats _all
The results have indicated that Joe Copula fits the data best.

Well, how can I get the Kendall tau measure of correlation?

Also, is there a way for me to get the residuals from both equations? I tried
Code:
predict varres, r equation (#1)
, but it doesn't look correct.

Thank you all.

Max