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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 10:22:31 +0100

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 (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.

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
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Faculty page:
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Personal page:
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____________________________________________________



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
>>>>
>>>>
>>>>
>>>> >>>>
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>>>
>>> --
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>> under charity
>>> number SC000278.
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>>
>> >>
>> *
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>
>

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