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RE: st: Bootstrapping & clustered standard errors (-xtreg-)

From   "Tobias Pfaff" <>
To   <>
Subject   RE: st: Bootstrapping & clustered standard errors (-xtreg-)
Date   Mon, 12 Sep 2011 17:51:48 +0200

Dear Stas, Bryan,

I was maybe not clear why I want to bootstrap at all:

My fixed effects regression with clustered SE works fine.
[-xtreg depvar indepvars, fe vce(cluster region) nonest dfadj-]

However, my predicted residuals (-predict res_ue, ue-) are not normally
Am I mistaken that I need normally distributed residuals for the
t-statistics to be unbiased?

If I'm not mistaken then I would like to do a robustness check with
bootstrapped standard errors (where the normal distribution of residuals
doesn't matter for the z-statistics to be unbiased) to see if my results
change or not.
And I still get the error message of insufficient observations when trying
to bootstrap with clustered SE. Using -idcluster()- does not help.
I have 76,000 obs., 8100 individuals, 108 clusters, and 36 regressors. I
don't think that the bootstrap would produce a sample with fewer cluster
id's than regressors.
So I still don't know why I get the error message after -xtreg depvars
indepvars, fe vce(bootstrap, reps(3) seed(1)) cluster(region_svyyear) nonest

Your arguments regarding the usage of weights were convincing. However,
-xtreg- only allows for weights that do not change for the individuals over
the years. Our panel dataset has a variable for the design weight that does
not change over the years, but this weight does not contain information on
non-response. Another weight variable in the dataset contains information on
selection probabilities and non-response, but it obviously changes over the
years for each individual, and cannot be used with -xtreg-. So I wouldn't
know how to incorporate information on non-response with -xtreg-?

Earlier in this thread Cameron said that bootstrap only makes sense in my
case if I would use "custom bootstrap weights computed by a statistical
agency for a complex sampling frame". It seems that bootstrap cannot be used
with weights, anyway. I guess that weighted sampling is still not
implemented in bootstrap, as stated 8 years ago

Thanks very much for your help,

P.S.: I cited the PNAS paper since it is a rare exception in my field
(happiness economics) that an empirical paper says something about
regression diagnostics at all.

-----Ursprüngliche Nachricht-----
> Date: Thu, 08 Sep 2011 17:20:35 -0400
> Subject: Re: st: Bootstrapping & clustered standard errors (-xtreg-)
> From: Bryan Sayer <>
> To:

        ... The
        sampling weights control mostly for unequal probabilities of
        selection, and for well-designed and well-conducted surveys,
        non-response adjustments are not that large, while probabilities of
        selection might differ quite notably.

I disagree with the part about non-response adjustments not being that
large. It really depends on the survey. Surveys in the U.S. may have
response rates as low as 25 to 30%, meaning that the non-response
adjustments may be pretty large.

However, it is really the difference in response rates for different groups
that matters. For example a survey I am working with shows a noticeable
difference in response rates between the land-line phone and the cell phone
only group.

The design effects for surveys can be broken into pieces for clustering,
stratification, and weighting. And weighting can be further classified into
the design weights and the non-response adjustments. If one really wanted to
pursue the matter.

But more related to the point Stas is making, often the elements of the
survey design and weights that are incorporated into the survey will reflect
information that is not available to the user. Simple put, it may not be
possible to fully condition on the true sample design. This is because some
of the elements used in the sample design and weighting process cannot be
disclosed in public files for confidentiality reasons.

Working in sampling, I am obviously biased toward using the weights. But
fundamentally, I believe that it is often impossible for the user to know
whether they have fully conditioned on the sample design or not.

Most likely, lots of smart people worked hard on the sample design and
everything that goes into producing the data that you are using. Accept that
they (hopefully) did their job well. So if you have the sample design
information available to you, I don't see any reason to *not* use it.

My impression is that bootstrapping of complex survey design data, while
possibly past its infancy, is probably still not very fully developed. I
know lots of very smart people who work on it, but it just does not seem to
generalize very well, at least not as well as a Taylor series linearzation.

Just my 2 cents worth.

Bryan Sayer
Monday to Friday, 8:30 to 5:00
Phone: (614) 442-7369
FAX:  (614) 442-7329

On 9/8/2011 4:28 PM, Stas Kolenikov wrote:


    I would say that you are worried about exactly the wrong things. The
    sampling weights control mostly for unequal probabilities of
    selection, and for well-designed and well-conducted surveys,
    non-response adjustments are not that large, while probabilities of
    selection might differ quite notably. While it is true that if you can
    fully condition on the design variables and non-response propensity,
    you can ignore the weights, I am yet to see an example where that
    would happen. Believing that your model is perfect is... uhm... naive,
    let's put it mildly; if anything, econometrics moves away from making
    such strong assumptions as "my model is absolutely right" towards
    robust methods of inference that would allow for some minor deviations
    from the "absolutely right" scenario. There are no assumptions of
    normality made anywhere in the process of calculating the standard
    errors. All arguments are asymptotic, and you see z- rather than
    t-statistics in the output. In fact, the arguments justifying the
    bootstrap are asymptotic, as well. You can still entertain the
    bootstrap idea, but basically the only way to check that you've done
    it right is to compare the bootstrap standard errors with the
    clustered standard errors. If they are about the same, any of them is
    usable; if they are wildly different (say by more than 50%), I would
    not either of them, but I would first check to see that the bootstrap
    was done right.

    I know that PNAS is a huge impact factor journal in natural sciences,
    but a statistics journal? or an econometrics journal? I mean, it's
    cool to have a paper there on your resume, but I doubt many statalist
    subscribers look at this journal for methodological insights (some
    data miners or bioinformaticians or other statisticians on the margin
    of computer science do publish in PNAS, though). I would not turn to
    an essentially applied psychology paper for advice on clustered
    standard errors.

    The error that you report probably comes from the bootstrap producing
    a sample with fewer cluster identifiers than regressors in your model.
    Normally, this would be rectified by specifying -idcluster()- option;
    however in some odd cases, the bootstrap samples may still be
    underidentified. I don't know whether the fixed effects regression
    should be prone to such empirical underidentification. It might be,
    given that not all of the parameters of an arbitrary model are
    identified (the slopes of the time-invariant variables aren't).

    On Thu, Sep 8, 2011 at 3:30 AM, Tobias Pfaff
    <>  wrote:

        Dear Stas, Cam,

        Thanks for your input!

        I want to bootstrap as a robustness check since my residuals of the
        regression are not normally distributed.
        And bootstrapping as a robustness check because it does not assume
        of the residuals
        (e.g., Headey et al. 2010, appendix p. 3,

        If I do bootstrapping with clustered standard errors as Jeff has
explained I
        get the following error message:

        - insufficient observations
        an error occurred when bootstrap executed xtreg, posting missing
values -

        Cam, you say that I would need custom bootstrap weights. My dataset
        individual weights with adjustments
        for non-response etc. I do not use weights for the regression
because the
        possible selection bias is mitigated due
        to the fact that the variables which could cause the bias are
included as
        control variables (e.g., income, employment
        status). Thus, I would argue that my model is complete and the
        analysis leads to unbiased estimators.

        1. Would you still include weights for the bootstrapping?

        2. Does bootstrapping need more degrees of freedom than the normal
        estimation of -xtreg- so that I get the above error message?

        3. If bootstrapping is not a good idea in this case, what can I do
        encounter the breach of the normality assumption of the residuals?
        (I already checked transformation of the variables, but that doesn't


        -----Ursprüngliche Nachricht-----

            Date: Wed, 7 Sep 2011 10:24:33 -0400
            Subject: RE: st: Bootstrapping&  clustered standard errors
            From: Cameron McIntosh<>

        Stas, Tobias
        I agree with Stas that there is not much point in using the
bootstrap in
        this case, unless you have custom bootstrap weights computed by a
        statistical agency for a complex sampling frame, which would
        adjustments for non-response and calibration to known totals, etc. I
        think that is the case here, so I would go with the -cluster- SEs
        My two cents,

            Date: Wed, 7 Sep 2011 09:03:27 -0500
            Subject: Re: st: Bootstrapping&  clustered standard errors


            can you please explain why you need the bootstrap at all? The
            bootstrap standard errors are equivalent to the regular
            standard errors asymptotically (in this case, with the number of
            clusters going off to infinity), and, if anything, it is easier
to get
            the bootstrap wrong than right with difficult problems. If
            option works at all with -xtreg-, I see little reason to use the
            bootstrap. (Very technically speaking, in my simulations, I've
            the bootstrap standard errors to be more stable than -robust-
            errors with large number of the bootstrap repetitions that have
to be
            in an appropriate relations with the sample size; whether that
            over to the cluster standard errors, I don't know.)

            On Tue, Sep 6, 2011 at 12:25 PM, Tobias Pfaff
            <>  wrote:

                Dear Statalisters,

                I do the following fixed effects regression:

                xtreg depvar indepvars, fe vce(cluster region) nonest dfadj

                Individuals in the panel are identified by the variable
"pid". The
                time variable is "svyyear". Data were previously declared as
                data with -xtset pid svyyear-.
                Since one of my independent variables is clustered at the
                level (not at the individual level), I use the option


                Now, I would like to do the same thing with bootstrapped


                I tried several commands, however, none of them works so
far. For


                xtreg depvar indepvars, fe vce(bootstrap, reps(3) seed(1)


                nonest dfadj
                .where I get the error message "option cluster() not

                None of the hints in the manual (e.g., -idcluster()-,


                in the main command) were helpful so far.

                How can I tell the bootstrapping command that the standard


                clustered at the regional level while using "pid" for panel

                Any comments are appreciated!

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