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st: anycount .a and .b
Jörg Eulenberger <email@example.com>
st: anycount .a and .b
Tue, 03 Jul 2012 19:08:33 +0200
I have a dataset with ".a" ".b" and "." missings. Now i want count the
missing row wise (observation wise) like
egen a_missing = anycount(var1-var600), values(.a)
egen b_missing = anycount(var1-var600), values(.b)
The Problem is that ".a" ".b" don't be elements of integer numlist.
rowmiss count all missings and make not differences between .a .b and .
Know everyone a solution?
Am 08.06.2012 15:21, schrieb Abhimanyu Arora:
> Hello again
> Just to follow up,
> I discovered a few things that might facilitate a healthy discussion.
> Vince Wiggins has got a post here
> http://www.stata.com/statalist/archive/2003-06/msg00646.html, that
> might be somewhat related.
> But as I infer from his answer to Mark, there's to be 1 one and rest
> zeros. But when I summarize my >2500 observations, I have 40% ones.
> Also I do get standard error for this variable if I cluster it 1-way
> (or I guess level would be more econometrically correct), instead of
> But I hope to understand this puzzling issue thanks to your cooperation
> Then I see that one-way clustering does
> On Fri, Jun 8, 2012 at 2:35 PM, Abhimanyu Arora
> <firstname.lastname@example.org> wrote:
>> Dear statalist
>> I am estimating a panel-fixed effects model with two-way clustering
>> with one endogenous variable making use of Schaffer et al's -xtivreg2-
>> . which xtivreg2
>> *! xtivreg2 1.0.13 28Aug2011
>> *! author mes
>> May I request you to shed some light on a couple of issues.
>> I am getting missing standard error for one included instrument. It is
>> a binary variable.
>> I get the following warning in my output indicating that this variable
>> is problematic.
>> Warning: estimated covariance matrix of moment conditions not of full rank.
>> 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.
>> However, since I would have liked to see the significance of the
>> coefficient of this variable, is there a way to address this problem
>> (inability to infer) without -partial-? I guess one might have to drop
>> observations...but is it so?
>> Another thing I need to clarify is that in this case while for weak
>> identification test using the KP rk Wald F statistic (=AP F) rejects
>> the null, I am not sure about the underidentification test---the first
>> stage reports Kleibergen-Paap rk LM statistic as well as AP chi-sq and
>> these two are meaning different conclusions for the null. For the
>> cluster-robust case which among the two is preferred, perhaps I might
>> have read too fast in the help file so as to miss it, so seek your
>> thoughts/references on this.
>> I could share the results if you ask.
>> With best wishes
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> * For searches and help try:
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