On Thu, 29 Nov 2012, Ranjan Maitra wrote:
Dear friends,
I am trying to understand and implement instrumental variables
regression using R.
I found a small (simple) example here which purportedly illustrates the
mechanics (using 2-stage least-squares):
http://www.r-bloggers.com/a-simple-instrumental-variables-problem/
The 1st stage regression is not quite correct in this example. My
understanding is that he wants to use distance as an instrument for
education and all other variables as instruments for themselves. However,
the rest of the instruments have been erroneously left out of the 1st
stage in his example.
Basically, here are the R commands (reproducible example) from that
site:
# ------ begin R
library(AER)
library(lmtest)
data("CollegeDistance")
cd.d<-CollegeDistance
simple.ed.1s<- lm(education ~ distance,data=cd.d)
This should be:
simple.ed.1s<- lm(education ~ urban + gender + ethnicity + unemp + distance,
data=cd.d)
cd.d$ed.pred<- predict(simple.ed.1s)
simple.ed.2s<- lm(wage ~ urban + gender + ethnicity + unemp + ed.pred ,
data=cd.d)
And then the same estimates could be obtained in ivreg() as:
m <- ivreg(wage ~ urban + gender + ethnicity + unemp + education |
urban + gender + ethnicity + unemp + distance, data = CollegeDistance)
Or in tsls() as:
m2 <- tsls(wage ~ urban + gender + ethnicity + unemp + education,
~ urban + gender + ethnicity + unemp + distance, data = CollegeDistance)
Best,
Z
# ------ end R
This yields the following summary:
summary(simple.ed.2s)
Call:
lm(formula = wage ~ urban + gender + ethnicity + unemp + ed.pred,
data = cd.d)
Residuals:
Min 1Q Median 3Q Max
-3.1692 -0.8294 0.1502 0.8482 3.9537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.053604 1.675314 -1.226 0.2203
urbanyes -0.013588 0.046403 -0.293 0.7697
genderfemale -0.086700 0.036909 -2.349 0.0189 *
ethnicityafam -0.566524 0.051686 -10.961 < 2e-16 ***
ethnicityhispanic -0.529088 0.048429 -10.925 < 2e-16 ***
unemp 0.145806 0.006969 20.922 < 2e-16 ***
ed.pred 0.774340 0.120372 6.433 1.38e-10 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 1.263 on 4732 degrees of freedom
Multiple R-squared: 0.1175, Adjusted R-squared: 0.1163
F-statistic: 105 on 6 and 4732 DF, p-value: < 2.2e-16
Question: Assuming that the above illustration is correct, I was
wondering how I could mimic these calculations using the ivreg () in
AER or tsls () in sem?
Any suggestions?
Many thanks in advance, and best wishes,
Ranjan
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