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Re: st: First stage F stats - xtivreg

From   Agnese Romiti <>
Subject   Re: st: First stage F stats - xtivreg
Date   Tue, 21 Jun 2011 19:06:07 +0200

Dear Austin,

When I used as cluster unit region-year or also only region I had to
run ivreg2 on the data that I have previously transformed in deviation
to the mean (within trasformation) because the xtivreg2 requires that
no panel overlaps more than one cluster. So panels should be uniquely
assigned to clusters.
 I tried to run instead xtivreg2 with two clusters as you suggested
but I received an error message  "cluster():  too many variables
specified", apparently  because I don't have the latest version of the
commands. I have just done an update all and my stata seems to be
updated to 30March 2011 (exe and ado), and to 1Sept 2010 , the
utilities. Is there a reason whereby  I still get the error?


2011/6/21 Austin Nichols <>:
> Agnese Romiti <>:
> I don't see how it matters that individuals move across clusters,
> unless you want to cluster by individual as well, and -xtivreg2-
> allows two dimensions of clustering. When you cluster by region-year,
> you assume that a draw from the dgp of person i in year t is
> independent from a draw from the dgp of person i in year t+1, which is
> clearly problematic.  You should try clustering by individual, by
> region, and then try two dimensions of clustering.  Let us know how
> the first stage diagnostic statistics and SEs on main variables of
> interest, in each of those 3 cases, compare to your
> region-year-clustered version.
> On Tue, Jun 21, 2011 at 10:47 AM, Agnese Romiti <> wrote:
>> Austin,
>> The reason whereby I have chosen the region-year as cluster unit was
>> due to the fact that individuals - around 8 percent of them - move
>> across regions over time, so the region  was not unique for them.
>> Many thanks again for your help and the ref.
>> Agnese
>> 2011/6/21 Austin Nichols <>:
>>> Agnese Romiti <>
>>> In that case the cluster-robust SE will be biased downward slightly,
>>> resulting in overrejection and your first-stage F stat overstated, but
>>> I expect it will still outperform the SE and F clustering by
>>> region-year.  You would have to do simulations matching your exact
>>> setup to be sure; see e.g.
>>> On Tue, Jun 21, 2011 at 3:27 AM, Agnese Romiti <> wrote:
>>>> Hi,
>>>> Thanks again
>>>> In my data I have 19 regions, and around 18 percent of the data in the
>>>> largest region.
>>>> Agnese
>>>> 2011/6/21 Austin Nichols <>:
>>>>> Agnese Romiti <>:
>>>>> No, you should cluster by region to correctly account for possible
>>>>> serial correlation,
>>>>> assuming you have sufficiently many regions in your data; how many are there?
>>>>> What percent of the data is in the largest region?
>>>>> On Mon, Jun 20, 2011 at 5:19 PM, Agnese Romiti <> wrote:
>>>>>> Many thanks Austin,
>>>>>> I'm actually clustering the standard errors at region-year level
>>>>>> rather than at region because I have one regressor with variability at
>>>>>> region-year level. Is that correct?
>>>>>> Do you think that the high first stage F stats might be a signal of a
>>>>>> bad instrument?Like a failure of the exogeneity requirement?
>>>>>> Agnese
>>>>>> 2011/6/20 Austin Nichols <>:
>>>>>>> Agnese Romiti <>:
>>>>>>> Are you clustering by region to account for the likely correlation of
>>>>>>> errors within region?
>>>>>>> Also see
>>>>>>> for an alternative model that allows your dep var to be nonnegative.
>>>>>>> On Mon, Jun 20, 2011 at 3:49 AM, Agnese Romiti <> wrote:
>>>>>>>> Dear Statalist users,
>>>>>>>> I'm running a fixed effect model with IV (xtivreg2) , my dependent
>>>>>>>> variable is a measure of labor supply at the individual level (working
>>>>>>>> hours). Whereas I have an endogenous variable with variation only at
>>>>>>>> regional-year level.
>>>>>>>> My question is about the First stage statistics, the Weak
>>>>>>>> identification test results in an F statistics extremely high which
>>>>>>>> makes me worry about something wrong, i.e. F=3289.
>>>>>>>> Do you have any clue about potential reasons driving this odd result?
>>>>>>>> Many thanks in advance for your help.
>>>>>>>> Agnese
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