Dear Statalisters
I am trying to estimate a model of market participation (sellers, non-sellers: given that someone sells, how much are they selling?) addressing the following issues:
i) complex survey design (PSUs and pweights only)
ii) sample selection bias
iii) potential reverse causality between regressors and dependent variable
To be able to use instrumental variables I think I will need to estimate the model in two steps (�by hand�) rather than using the heckman command. However, because of the inverse mills ratio in the outcome equation, this means that I also need to make an adjustment to the covariance matrix of the outcome equation so that I get correct standard errors. I�ve looked through stata FAQs and statalist and trawled the internet and the closest I can find to what I want to do is set out below, minus the IV part of the estimation for now for simplicity (I took the syntax from Vince Wiggins' FAQ post "Must I use all of my exogenous variables as instruments when estimating instrumental variables regression?")
However, there is a problem in that by using svy:regress Stata does not seem to give e(rmse) so the new Vmatrix ends up empty. Is there a way of recovering the estimated rmse so I can plug it into the formula? Or is there a better way for me to do this? I have been grappling with this for some time, so any help (solutions or encouragement to let this one go) very much appreciated.
Many thanks
Jennifer
The syntax:
/*selection equation*/
svy: probit y2 x w
predict Z, xb /*fitted values*/
gen mills=normden(Z)/norm(Z)
/*Outcome equation*/
svy: regress y1 mills x if y2==1
set more off
rename Z y2hold
rename y2 Z
predict double res, residual
rename Z y2
rename y2hold Z
replace res=res^2
summ res
scalar realmse = r(mean)*r(N)/e(df_r)
matrix bmatrix = e(b)
matrix Vmatrix = e(V)
matrix Vmatrix = e(V) * realmse /e(rmse)^2 /*stata does not return e(rmse) - dividing by zero in that case*/
ereturn post bmatrix Vmatrix, noclear /*so the Vmatrix is empty*/
ereturn display
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