Stata The Stata listserver
[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: collinearity in 2SLS


From   Kit Baum <baum@bc.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: collinearity in 2SLS
Date   Thu, 15 Sep 2005 20:33:04 -0400

Tinna said

I tried to rescale the endogenous variable... actually used your pick
of numbers (1000). It sure did shrink the endogenous variables
coefficient. However, it shrunk according to the scaling, so the
interpretations stays the same. And the number is way to large to make
sense in that context.

The results are below. The first coefficient is the one of interest,
and it did change as I mentioned before. However it only changed in
number, but not in terms of the real effect DP1 has on the dependent
variable. I tried rescaling the instrument also, but it didn't do
anything either.

Sorry if I am being slow here. My code is below.

Tinna

. generate dailysmoke1000= dailysmoke*1000

. ivreg hrstotal centage centagesq (DP1= dailysmoke1000 ) edu2
edu3 edu4 edu5 edu6 marr2 marr3 marr4 ch
> ildren health if male==1 & empl3!=1 & empl5!=1 & empl7!=1

(snip)

I would first of all try ivreg2 (ssc install ivreg2) on this same specification. Your equation is exactly identified, so the results are heavily dependent on how good your single excluded instrument might be in explaining the DP1 variable. Are there other plausible instruments that might be used, so you could use an overidentification test? I don't think this is a problem of collinearity in any way. I would also use mfx and calculate elasticity estimates before judging that the coefficient on this variable is 'too large'.


Kit Baum, Boston College Economics
http://ideas.repec.org/e/pba1.html


*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/




© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index