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03-30-2015 09:23 AM

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?

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Solution

08-09-2017
02:22 PM

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Posted in reply to SJONES_SASUSER

03-30-2015 04:39 PM

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.

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Solution

08-09-2017
02:22 PM

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Posted in reply to SJONES_SASUSER

03-30-2015 04:39 PM

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.