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From |
Robert Davidson <rhd773@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: ivreg2 questions |

Date |
Tue, 20 Mar 2012 10:09:08 -0400 |

Thank you for the response; it is quite helpful. Rob On Tue, Mar 20, 2012 at 8:02 AM, Christopher Baum <kit.baum@bc.edu> wrote: > <> > On Mar 20, 2012, at 2:33 AM, Rob wrote: > >> Sorry for what is purely an econometric question at this point >> (removed from Stata) but there is still one thing that I am >> misunderstanding. In every text I can read, it basically says the >> instrument must be correlated with the endogenous regressor (including >> Mostly Harmless Econometrics and an Introduction to Modern >> Econometrics Using Stata to name 2 - the latter stating the instrument >> must be highly correlated). These texts do not state that the >> instrument must have a high correlation with the endogenous regressor >> with the effect of a set of controlling variables removed (partial >> correlation). Is this just a simplification on the part of these >> texts or again is there something I am missing? And does this >> basically mean that the validity of an instrument is conditional on >> the other independent variables included in the primary model and not >> just the dependent variable and the endogenous regressor? > > Yes. It should be understood that when we say that an excluded instrument be highly correlated with the endogenous variable(s), > that correlation is a partial correlation, reflected by the partial regression coefficient in the 'first stage regression'. Consider > a case where Fahrenheit temp is an included exogenous regressor, and you attempt to use Celsius temp as an excluded > instrument. The instrument matrix will be rank-deficient. Now consider using Fahrenheit temp + epsilon as an excluded instrument, > where epsilon is random noise. The 'first-stage regression' (projection of endog on all exog) will be computable, but > the marginal value of your excluded instrument is very low, as it really contains no marginal information that can be used > to identify the model. So even though temperature may be highly correlated to the endogenous regressor (say, quantity traded > in the market), and you have satisfied the rank and order conditions, the model has weak instrument problems which relate > to the very low partial correlation. I suppose we could speak of the simple correlation between endogenous and excluded > instrument if we added the caveat that the instrument matrix was far from ill-conditioned, but that is a harder concept > to motivate and test. > > Kit > > Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html > An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html > An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Re: ivreg2 questions***From:*Christopher Baum <kit.baum@bc.edu>

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