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Re: st: how to adjust covariance matrix in two-stage model using svy (and iv)?


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: how to adjust covariance matrix in two-stage model using svy (and iv)?
Date   Fri, 28 Mar 2008 13:49:37 -0400

Jennifer Leavy <J.Leavy@ids.ac.uk>:
How about using -ivpois- from SSC instead?  Non-sellers are those who
sell zero units; in a Poisson regression that fits the data well, they
would have very small predicted sales, so that the rounded predicted
value would be zero.  -ivpois- implements a GMM version of Poisson
that allows endogenous regressors and excluded instruments.  Such a
choice is similar to a choice among a two-part model, -zip-, or
-poisson-; see also http://www.nber.org/papers/t0228 or

Mullahy, John. 1998. "Much Ado About Two: Reconsidering
Retransformation And The Two-Part Model In Health Econometrics,"
Journal of Health Economics, 17(3): 247-281.


On Fri, Mar 28, 2008 at 12:33 PM, Jennifer Leavy <J.Leavy@ids.ac.uk> wrote:
> 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?")
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