Announcement

Collapse
No announcement yet.
X
  • Filter
  • Time
  • Show
Clear All
new posts

  • Sample selection corrections for focal variable

    Dear Statalists,

    I am struggling in addressing sample selection bias for my focal variable, which is a dummy. My study is to investigate the effects of a technology T on firm performance. In doing so, I have constructed a dummy variable DT as a proxy for the adoption: 1 if the firm adopt technology T and 0 otherwise. In fact, I have gone through firms' annual reports to check their adoption. The selection thus include sample selection bias because firm does not confirm "I don't adopt/use T in my company". In this sense, DT=0 presents missing values rather than "NOT" adopted.
    I have tried to use control function approach (by adding generalized residuals in the regression). But the reviewers ask me to use inverse Mill ratio, by regressing a probit model, for further analysis (But they did not explain so much on this). I have took a look at Heckman two-stage procedure but it seems to exclude DT at the last phase.
    Can I hear something from you all to address this issue? These are what I have done by using control function approach:
    1. Regress the probit model for DT: Pr(DT=1!X,Z) = a + biXit + ciZit + ei
    2. Calculate generalized residuals GR suggested by Wooldridge (2015)
    3. Add GR as a new variable in the baseline model: Yit=a + biXit + c*DT + d*GR + eit
    My focus is c

    Thanks so much for your time!
Working...
X