RE: RE: Re: Re: Re: Re: st: a user-written program for clustering SE on more than one clustering variable?

["Ariel Linden, DrPH" <ariel.linden@gmail.com>]

As a follow-up to this thread, I have contacted Douglas Miller who is one of
the authors on the multi-way clustering paper that I cited earlier in the
thread http://www.stata.com/statalist/archive/2013-08/msg00396.html

My question to him was whether they have expanded the -cgmreg- software to
other data types. He pointed me to this site:
https://webspace.utexas.edu/jc2279/www/data.html where in fact there are
programs for additional distributional types.

I hope others interested in this topic find this useful...

Ariel




-----Original Message-----
From: Ariel Linden, DrPH [mailto:ariel.linden@gmail.com] 
Sent: Saturday, August 10, 2013 7:34 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: Re: Re: Re: st: a user-written program for clustering SE on
more than one clustering variable?

Nice synopsis, Stas! This mirrors what Rabe-Hesketh & Skrondal (2005)*
conclude on page 223 after describing longitudinal, panel and growth-curve
models.

Ariel

Rabe-Hesketh, S. and Skrondal, A. (2005). Multilevel and Longitudinal
Modeling using Stata. College Station, TX: Stata Press.



________________________________________
From
  Stas Kolenikov <skolenik@gmail.com>
To
  "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject
  Re: Re: Re: st: a user-written program for clustering SE on more than one
clustering variable?
Date
  Sat, 10 Aug 2013 11:14:53 -0500
________________________________________
Defensibility depends on who you talk to. In my experience, economists
would be happier with the clustered standard errors, as this method
makes fewer assumptions about the data (like the specific structure of
the error terms that -xtmixed- has to assume... although it still
makes some difficult to assess assumptions of uncorrelatedness of e_it
and e_js terms for i!=j, t!=s; violation of this assumption is what
produces the negative variance estimates). For health scientists,
mixed models are far better understood, give smaller standard errors
in small samples (which is what health people apparently have to deal
with more often than economists) and the context of the clustered
standard errors is mostly that of GEE. Education and psychology people
have little clue about clustered standard errors at all, as nearly all
of their models are multilevel models (even the name is different!).

If I were to see drastically different results from -regress- and
-xtmixed-, I would rather want to take -regress- more seriously, as it
makes fewer assumptions. Getting the right standard errors is going to
be a big pain in the lower back, but if I wouldn't get what I needed
from -cmgreg- and the like (once again, -ivreg2- must be able to do
this, as far as I can recall, and is in Stata mainstream, meaning
better coded, better documented and better understood by the user
base), I would look at Art Owen's multiway bootstrap
(http://www.citeulike.org/user/ctacmo/article/11402489), although
again it makes assumptions similar to those underlying -cmgreg-
regarding independence of observations that do not overlap on any
dimensions.

-- Stas Kolenikov, PhD, PStat (ASA, SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
-- http://stas.kolenikov.name




*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/