[Limdep Nlogit List] Wts= option in Random Parameter Models. Consistent? Correct Covariance Estimates?
Stephen McLaughlin
stephen.mclaughlin at yale.edu
Tue Feb 2 07:40:48 AEDT 2016
Hi Limdep/NLogit Listserv,
I'm curious about the use of sampling weights in random parameter models in
NLogit.
Solon, Haider, and Wooldridge (2011) lay out situations where it is
advisable to use a weighted estimation approach, and note that in cases
where sampling is not independent of the dependent variable, conditional on
the explanatory variables, then one needs to use weighted estimation to
generate consistent parameter estimates. It seems to me that many cases
would fit this description, e.g. non-participation in the US National
Survey on Drug Use and Health is likely related to whether an individual
has a history of drug use.
Inclusion of Wts seems reasonable in many cases, however in Applied Choice
analysis, pg. 854 there's a brief paragraph about the use of choice-based
weights in estimation of ML models and how NLogit attempts to handle these
situations. The paragraph concludes 'we warn analysts who might unwittingly
assume that choice-based weights apply without question to ML models'.
Choice based weights strike me as very similar to sampling weights that are
adjusted to match a target population total, where the population total
happens to be an outcome of interest.
My question then is: Is inclusion of sampling weights 'reasonable' in
simulation based models? Are there particular issues that an analyst should
be wary of? In general, will inclusion of sampling weights in mixed logit,
random parameters ordered probit, etc. in NLogit still produce consistent
parameter estimates, and will the standard errors be 'correct'?
Any advice or references is greatly appreciated. I can also provide a more
specific example if it helps.
Cite:
Solon et al. 2011 *What Are We Weighting For?*
http://www.nber.org/papers/w18859
--
Mike McLaughlin
Ph.D. Student, Yale School of Public Health
NIDA Pre-Doctoral Fellow
stephen.mclaughlin at yale.edu | 301.367.0788
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