[R] robust standard errors in maximum likelihood estimation; sandwich estimator for mle/mle2
Marc Jekel
feuerwald at gmx.de
Wed Jul 2 18:19:42 CEST 2014
Dear list,
After more reading, I can specify my rather broad question I asked yesterday
and therefore ask a better question: I have specified a function that gives
me log likelihood values. In the function, I have several free parameters
(the function itself is not linear). I use mle2 to find the maximum
likelihood estimators for all free parameters. When I use summary() on the
object created by mle2 I get the maximum likelihhod estimators, standard
errors, corresponding z-values and Pr(z).
My problem: The data I fit the function to consists of repeated choices by
multiple participants. This means I have to correct standard errors that are
shown by summary() since these standard errors are calculated under the
assumption that each choice is independent. From what I read is that I need
the sandwich estimator (i.e., Huber) to estimate robust errors. This
estimator is implemented in the R-library "sandwich". But, as far as I found
out, the library needs an object of the (e.g.) type lm. An object resulting
from mle2 cannot be used with the commands of the package. In STATA maximum
likelihood estimation with robust standard errors is easily implemented with
he command "cluster(id)". Is there something similar in R?
Thank you for any advice,
Marc
Gesendet: Dienstag, 01. Juli 2014 um 10:07 Uhr
Von: "Marc Jekel" <feuerwald at gmx.de>
An: r-help at r-project.org
Betreff: maximum likelihood estimation with clustered data
Dear list,
I am currently trying to fit free parameters of a model from economics
(cumulative prospect theory) using maximum likelihood estimation. I know how
to do maximum likelihood estimation using mle or mle2 in R, the problem to
which I could not find a solution to is that my data is correlated (i.e.,
multiple participants with multiple responses) which needs to be accounted
for when doing mle. In STATA, mle can be done with clustered data (with the
command "ml model ..., cluster(id)") but I could not find an equivalent
command in R.
More detail (in case someone tried to do the same before): I try to
implement an approach proposed by Glenn Harrison who shows in STATA how to
implement user-written maximum likelihood estimates for utility functions
with clustered
data ([1]http://faculty.cbpp.uaa.alaska.edu/jalevy/protected/HarrisonSTATML.
pdf).
Thank you for any hint,
Marc
References
1. http://faculty.cbpp.uaa.alaska.edu/jalevy/protected/HarrisonSTATML.pdf
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