Re: st: Clustered Standard Errors vs HLM for Small Sample Project

[John Antonakis <John.Antonakis@unil.ch>]

Thanks for the clarifications Austin.

In fact, I do not think I mixed up what you suggest I did. It seemed to 
me that the original question was whether MK should use "clustered 
standard errors or HLM".

I assumed that:

1. "clustered standard errors" = pooled OLS with cluster-robust standard 
errors (I did not assume that MK was suggesting that this estimator was 
OLS with FE dummies, which case that OLS estimator is also potentially 
inconsistent if the FEs are omitted)

2. HLM = RE model.

My suggestion was that before estimating a RE model MK should first 
ensure that the RE estimator is consistent (and  I suggested the 
xtoverid command for the Hausman test).

For the cluster size, I am happy to see that you cite Kézdi regarding 50 
clusters is probably sufficient for valid inference. I did not pay 
attention to the unbalanced clusters issue because I was so focused on 
the RE problem; thanks for catching that.

BTW, any idea what has happened to cltest and xtcltest (you cite on p. 
23 of your presentation)?

Best,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor:
The Leadership Quarterly
Organizational Research Methods
__________________________________________

On 18.11.2013 21:19, Austin Nichols wrote:
> For good inference, you want not only many clusters, but also clusters
> that are balanced (which means guidelines about 20 or 30 or 42 or 50
> clusters are less than helpful):
> http://www.stata.com/meeting/13uk/nichols_crse.pdf
>
> When RE/HLM models and Cluster-Robust SE work well, they give similar
> answers, but in some circumstances where they work poorly, they can
> also give similar (wrong) answers:
> https://appam.confex.com/appam/2013/webprogram/Paper6337.html
>
> You need to describe in more detail the source of correlations in
> errors and regressors to get a good answer--on how to design a
> simulation to indicate which approach is likely to give the best
> inference in your setting.
>
> In his reply below, John Antonakis seems to be mixing up a comparison
> between FE and RE (ssc describe xtoverid) with a comparison between FE
> and pooled OLS with CRSE; whether or not you should use a fixed
> effects method is a more complicated question than any one test will
> answer, and depends very strongly on what you believe about
> measurement error in your predictors.
>
> On Mon, Nov 18, 2013 at 10:26 AM, John Antonakis 
<John.Antonakis@unil.ch> wrote:
>> Hi:
>>
>> You should not use terms like "HLM" (which is a program in addition 
to an
>> estimation method in some disciplines) without defining it (most 
here do not
>> use this program but Stata obviously).
>>
>> I guess I know what you are after, that is, whether you should 
estimate a
>> random-effects (multilevel model), versus a pooled model using OLS 
with a
>> cluster-robust estimate of the variance--. Before you do anything, 
and if
>> you have level 1 (i.e., within cluster varying predictors), then you 
should
>> be much more worried about omitted fixed-effects than just about robust
>> standard errors--which are important too. See:
>>
>> Halaby, C. N. 2004. Panel models in sociological research: Theory into
>> practice. Annual Review of Sociology, 30: 507-544.
>>
>> So, I would first check for omitted fixed-effects. If the Haumsan
>> endogeneity test (can be tested with the user written command -xtoverid-
>> from SSC) is significant, it means that he restrictions that your 
regressors
>> don't correlate with the uj (i.e., the fixed-effect error term) is 
rejected.
>> Then you either must model the fixed effects either with dummies or 
using
>> the Mundlak procedure:
>>
>> Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2010. On making
>> causal claims: A review and recommendations. The Leadership Quarterly,
>> 21(6): 1086-1120.
>>
>> Next, as for the number of clusters ideally you'll have between 
30-50 for
>> valid inference.
>>
>> Hth.
>> J.
>>
>> __________________________________________
>>
>> John Antonakis
>> Professor of Organizational Behavior
>> Director, Ph.D. Program in Management
>>
>> Faculty of Business and Economics
>> University of Lausanne
>> Internef #618
>> CH-1015 Lausanne-Dorigny
>> Switzerland
>> Tel ++41 (0)21 692-3438
>> Fax ++41 (0)21 692-3305
>> http://www.hec.unil.ch/people/jantonakis
>>
>> Associate Editor:
>> The Leadership Quarterly
>> Organizational Research Methods
>> __________________________________________
>>
>>
>> On 18.11.2013 03:06, mkobren1@comcast.net wrote:
>>>
>>> I'm using STATA 10 and I'm trying to figure out whether to use 
clustered
>>> standard errors or HLM.I have 233 observations from agencies 
located in 10
>>> different states.
>>>
>>> The minimum number of observations I have from a state is 3 and the
>>> maximum number of observations I have is 108 with an average
>>> of 23.3. I'm not interested in state level differences, I'm only
>>> interested in results from the agency level and I want to account 
for the
>>> fact that there may be some state level effects.
>>>
>>> The literature I've read so far doesn't seem to point me in any 
definite
>>> direction. The literature seems to say that HLM works best on larger
>>> datasets, but it also seems to say that you need at least 20 
clusters for
>>> either method to be effective. Does anyone have a suggestion for 
which of
>>> these two methods I should use, or at least what I should consider 
in making
>>> my choice? Is there some other method I should use?
>>>
>>> Thank you in advance for your consideration.
>>>
>>> MK
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