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Re: st: question on ivreg2

From   "Mark Schaffer" <[email protected]>
To   [email protected]
Subject   Re: st: question on ivreg2
Date   Wed, 31 Mar 2004 15:02:43 +0100

Thi Minh,

Subject:        	st: question on ivreg2
Date sent:      	Tue, 30 Mar 2004 22:40:35 +0100
From:           	"Ngo,PT  (pgr)" <[email protected]>
To:             	<[email protected]>
Send reply to:  	[email protected]

> Dear Statalisters,
> I would like to send a big thank you to Christopher Baum, Mark
> Schaffer and Steven Stillman for their excellent, very useful  paper
> "Instrumental variables and GMM: Estimation and Testing", and for
> developing ivreg2.
> Could someone help answer questions that arose when I tried to use
> ivreg2? Pls forgive me if they are basic questions.
> 1- In the examples shown in the help file for ivreg2, the test for
> heteroskedasticity in IV/GMM estimation is indicated with the option
> fitlev (fitted values). Why is this option needed ? I obtain different
> results when including (cannot reject null of homoskestaticity) and
> omitting (reject null of homoskestaticity) the fitlev option (see
> output below).

The trade-off is between the number of degrees of freedom consumed 
(can be huge in some cases, including yours) and the ability of the 
test to detect heteroskedasticity.  If you look in lots of directions 
(full set of levels, squares and cross-products), you might find, 
roughly speaking, any type of heteroskedaticity, but your ability to 
detect it will be weak.  If you look in one direction (fitlev), 
you'll be able to find only certain narrowly defined types of 
heteroskedasticity, but your ability to find it will be strong(er).

> 2- What are centered and uncentered R-square ? I do not understand why
> in some of the ivreg I ran, I also obtained negative r-squares.

Have a look at Davidson & MacKinnon (1993) or Wooldridge (2000 or 
2002).  Centered is the usual output of regression packages, but 
uncentered is sometimes needed, esp. in artificial regression 


> 3- I am using ivreg2 on a two-year panel data, on variables that have
> been first differenced. One variable may be endogenous (criyielA) and
> would need to be instrumented for. In their paper, Baum et al caution
> against the use of  instrument with little explanatory power and
> propose a rule of thumb of an F-stat above 10. It is very difficult
> for me to find relevant excluded instruments and the F-stat is below
> 10. What would be your advice ?
> Thanks a lot in advance for your help,
> Best regards,
> Thi Minh
> PS: example of output below

Prof. Mark E. Schaffer
Centre for Economic Reform and Transformation
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS  UK
44-131-451-3494 direct
44-131-451-3008 fax
44-131-451-3485 CERT administrator
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