- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content
I am trying to determine if there is possibility unmeasured bias in my model. I have a continuous dep variable and binary ind variable which is TREATED (0/1). I want to determine if I have unmeasured bias. I don't have proc qlim so I need to create an inverse Mills ratio and run it through a GLM.
I first create a PROBIT model and output the estimated probabilities (prob) of being treated.
Next, I calculate the Inverse Mills Ratio:
IMR = pdf('NORMAL', prob ) / cdf('NORMAL', prob ); /*inverse mills ratio*/
Then run my GLM:
proc glm data = weighted_PS;
class RHS;
model LHS = RHS IMR/ ss3 solution;
weight weights;
run;
Is this correct?
Accepted Solutions
- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content
You are correct that PROC QLIM is the most efficient, accurate and simplest way to accomplish Heckman's 2-step estimator.
You will need to correct your standard errors in your second stage. See the QLIM documentation. SAS/ETS(R) 13.2 User's Guide
You might just want to use PROC REG with some HCCME= options to correct your standard errors. SAS/STAT(R) 9.2 User's Guide, Second Edition
But you are close as is.
- Mark as New
- Bookmark
- Subscribe
- Mute
- RSS Feed
- Permalink
- Report Inappropriate Content
You are correct that PROC QLIM is the most efficient, accurate and simplest way to accomplish Heckman's 2-step estimator.
You will need to correct your standard errors in your second stage. See the QLIM documentation. SAS/ETS(R) 13.2 User's Guide
You might just want to use PROC REG with some HCCME= options to correct your standard errors. SAS/STAT(R) 9.2 User's Guide, Second Edition
But you are close as is.