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Re: st: RE: ivreg versus xtivreg


From   John Antonakis <[email protected]>
To   [email protected]
Subject   Re: st: RE: ivreg versus xtivreg
Date   Fri, 12 Feb 2010 22:01:59 +0100

Interestingly, the Hansen J is non significant when I specify the model with the "robust" vce option. However, I cannot obtain the J test when clustering (as mention, I get the "warming" message).

Intuitively, would you think that the Hansen J would be close to that obtained from the two-way clustered J? I guess that there is no "trick" of sorts or something that could be done to get an idea if the overidentifying restrictions are viable.

Best,
J.

____________________________________________________

Prof. John Antonakis, Associate Dean Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 12.02.2010 17:53, Schaffer, Mark E wrote:
Thanks, John.  2-way clustering is terra nova, and I am interested in general about how it works in practice.  It sounds like your application is very suitable.

Cheers,
Mark

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of John Antonakis
Sent: Friday, February 12, 2010 4:04 PM
To: [email protected]
Subject: Re: st: RE: ivreg versus xtivreg

Hi Mark:

The SEs are quite similar (and still significant) using two-way clustering as compared to the robust or clustering on one dimension. The first dimension has 192 clusters and the second dimension has 99 clusters (and observations are n =433).

Regards,
John.

____________________________________________________

Prof. John Antonakis, Associate Dean Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 12.02.2010 14:26, Schaffer, Mark E wrote:
John,

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of John Antonakis
Sent: Friday, February 12, 2010 12:03 PM
To: [email protected]
Subject: Re: st: RE: ivreg versus xtivreg

Hi Mark:

Yes; I am using the 2-way cluster option. Fyi, the highest
n-size for
the cluster is 192.
I take it that means you've got 192 clusters in one
dimension. Do you have a similarly large number in the second dimension? In that case I agree, you're probably OK.
I'm also curious about the results of using 2-way
clustering.  Does it make a big difference?
--Mark

Glad to hear that the SEs are probably OK.

Best regards,
John.

____________________________________________________

Prof. John Antonakis, Associate Dean Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 12.02.2010 12:50, Schaffer, Mark E wrote:
John,

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of John Antonakis
Sent: Friday, February 12, 2010 9:23 AM
To: [email protected]
Subject: Re: st: RE: ivreg versus xtivreg

Hi Mark:

I have a similar error; however, the message I get is:

Warning: estimated covariance matrix of moment conditions not of full rank.
         overidentification statistic not reported, and
standard errors and model tests should be interpreted with caution.
Possible causes:
number of clusters insufficient to calculate robust covariance matrix singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)
partial option may address problem.

Note, I have modeled two dimensions of clustering
Do you mean you're using the new 2-way cluster option?

(and put all my fixed-effects in the partial option). Thus, I am correct in saying that the z test for a particular coefficient estimate is still interpretable.
Probably. I can think of some special cases where things
can go wrong (these are what motivated the writing of the warning message). You might have a small number of clusters (so the asymptotics are dodgy) or a singleton dummy which would mean you couldn't do a test on the significance of the coefficient (of course, you couldn't do that anyway). But most likely the SEs are OK.
--Mark

Best,
J.

____________________________________________________

Prof. John Antonakis, Associate Dean
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 12.02.2010 09:55, Schaffer, Mark E wrote:
 > Sara,
 >
 >> -----Original Message-----
 >> From: [email protected]
 >> [mailto:[email protected]] On Behalf Of
 >> sara borelli
 >> Sent: 12 February 2010 07:58
 >> To: [email protected]
 >> Cc: Schaffer, Mark E
 >> Subject: R: st: RE: ivreg versus xtivreg
 >>
 >> Hi Mark,
 >> I am sorry I did not understand your answer...and I think it
 >> is because I I made a typo in writing the command and I did
 >> not explain myself clearly. I do NOT include  i.county  in
 >> xtivreg2. So I reformulate my question:
 >>
>> xtivreg2 CRit (Xit=Wit) Zit (state by year
fixed effects),
 >> fe i(county), cluster(county)
 >>
 >> gives the error message " estimated covariance matrix of
 >> moment conditions not of full rank; overidentification
 >> statistic not reported, and standard errors and  model tests
>> should be interpreted with caution. Possible causes: >> singleton dummy variable (dummy with one 1 and N-1 0s or vice
 >> versa) fwl option may address problem"
 >>
 >> but the same command xtivreg2 without state by year effects:
 >> xtivreg2  CRit  (Xit=Wit)  Zit, fe i(county), cluster(county)
 >>  does NOT report the above error message
 >>
 >> I do not uderstand why the inclusion of state by year fe
 >> caused that error message
 >
> I think your explanation in your earlier email is
probably right:
 >
>> I noticed that 2 states have a number of counties
(clusters) lower
>> than the number of years available. By dropping these two states STATA
 >> run the xtivreg2 without giving the error message.
 >>
>> I have read the help file and FAQ and I think something is going with >> those state by year effects that creates a kind of
singleton dummy
 >> problem, but I am not sure Any help would be appreciated
 >
> And the question is, should you worry about it? The VCV should still be OK for tests of, say, one parameter at a time. So long
as you are
aren't trying to do something that requires a full-rank VCV (like 2-step GMM, or using an overid stat, or joint testing of all
the coeffs),
you're OK, I think.
 >
> Memo to self: calling this an "error" in the output
is possibly
overstating the issue; maybe "warning" is better.
 >
 > --Mark
 >
 >> thank you
 >> sara
 >>
 >>
 >>
>> --- Gio 11/2/10, Schaffer, Mark E <[email protected]> ha scritto:
 >>
 >>> Da: Schaffer, Mark E <[email protected]>
 >>> Oggetto: st: RE: ivreg versus xtivreg
 >>> A: [email protected]
 >>> Data: Giovedì 11 febbraio 2010, 19:43
 >>> Sara,
 >>>
 >>>> -----Original Message-----
 >>>> From: [email protected]
 >>>> [mailto:[email protected]]
 >>> On Behalf Of
 >>>> sara borelli
 >>>> Sent: Thursday, February 11, 2010 6:32 PM
 >>>> To: [email protected]
 >>>> Subject: st: ivreg versus xtivreg
 >>>>
 >>>> Dear members,
 >>>> I am running the following regressions in STATA8.2:
 >>>> CRit =  a*Xit +  b*Zit  + (state by
 >>> year fixed effects)  +
 >>>> (county fixed effects), cluster(county)
 >>>>
 >>>> CRit= crime rate in county i year t
 >>>> Xit = endogenous regressor
 >>>> Zit = set of 7 exogenous regressors
 >>>> state by year fixed effects = interactions between
 >>> indicators
 >>>> for each state and year
 >>>>
 >>>> I instrument Xit using Wit
 >>>> There are 246 counties, 10 states, 8 years I have been
 >> running this
 >>>> model with two commands
 >>>>
 >>>> xi:   ivreg  CRit
 >>> (Xit=Wit)  Zit   (state by year fixed
 >>>> effects)  i.county, cluster(county)
 >>>> xtivreg2  CRit  (Xit=Wit)
 >>> Zit   (state by year fixed
 >>>> effects)  i.county, fe i.(county)
 >>> cluster(county)
 >>>> the two commands give exactly the same coefficient,
 >>> and just
 >>>> slightly different std errors, but after xtivreg2 I
 >>> get the
 >>>> following message:
 >>>> Error: estimated covariance matrix of moment
 >>> conditions not
 >>>> of full rank;
 >>>> overidentification statistic not reported, and
 >>> standard
 >>>> errors and       model tests
 >>> should be interpreted with
 >>>> caution. Possible causes:  singleton dummy
 >>> variable (dummy
 >>>> with one 1 and N-1 0s or vice versa) fwl option may
 >>> address problem.
 >>>> I noticed that 2 states have a number of counties
 >>> (clusters)
 >>>> lower than the number of years available. By dropping
 >>> these
 >>>> two states STATA run the xtivreg2 without giving the
 >>> error message.
 >>>> I have read the help file and FAQ and I think
 >>> something is
 >>>> going with those state by year effects that creates a
 >>> kind of
>>>> singleton dummy problem, but I am not sure Any
help would be
 >>>> appreciated
 >>> It's not a problem.
 >>>
>>> You will note that xtivreg2 does not report the fixed effect dummy
 >>> variables.
 >>>
 >>> xi: ivreg2 explicitly includes the dummies, and the
 >> coefficients are
 >>> reported.
 >>>
 >>> The warning message reported by ivreg2 is triggered by the
 >> fact that
 >>> there are now many more rows/columns in the VCV, the new,
 >> extended VCV
 >>> is no longer full rank.
 >>>
 >>> In fact, the coefficients and standard errors for the
 >> dummies aren't
>>> consistent anyway (under the usual panel data assumptions - this is
 >>> the "incidental parameters problem").  So you really aren't
 >> interested
 >>> in the extended VCV anyway.
 >>>
 >>> HTH,
 >>> Mark
>>> >>>> thank you
 >>>> sara
 >>>>
 >>>>
 >>>>
>>>> >>>>
 >>>> *
 >>>> *   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/
 >>>>
 >>>
 >>> --
 >>> Heriot-Watt University is a Scottish charity registered
 >> under charity
 >>> number SC000278.
 >>>
 >>>
 >>> *
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 >>
>> >>
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 >
 >

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