Sure. Some missingness in all variables is not a problem for Amelia.
Gary
--
*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS - Harvard University
GKing.Harvard.edu <http://gking.harvard.edu/> - King(a)Harvard.edu -
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On Tue, Sep 7, 2010 at 4:15 AM, Jennie Day <jennieday(a)gmail.com> wrote:
Hi out there!
I hear Amelia is amazing. I've always been a Stata user, but I'm happy to
convert if someone can help me with this problem I have.
I am trying to to perform multiple imputation on a panel dataset where all
variables have some missing values (save the unique ID numbers and the time
variable). The missing rate varies from 1% to 10%.
I would like to ask what options Amelia offers me to move forward. Stata
is a dead end, I think. I understand the issues with this level of
missingness. Listwise deletion is not an option because 1) the missingness
is very likely correlated with other observed variables (e.g., income), so
the missingness is MAR at best, and 2) it would make the sample size too
small to be useful. STATA doesn't offer pairwise deletion, so I'd have to
code this up myself. And plus - according to a Stata listserv thread -
pairwise deletion generates worse biases than listwise according to (Allison
2002).
So here's my situation: I have a rich panel dataset from a developing
country that could yield some interesting policy results. It is the
unfortunate consequence of working on data from a developing country, that
the data has missing values. I've tried the mi functions in Stata using mvn
(the multivariate normal estimation option), and I get error messages like
the ones copied below. I've read in STATA's MI manual that doing univariate
estimations for multiple imputations is incorrect procedure if the results
are not used in independent analyses. I understand this, but it may be my
only option.
Can Amelia help me?
Thanks,
jennie
ERROR MESSAGES
1)
Iteration 0: variance-covariance matrix (Sigma) is not positive definite
posterior distribution is not proper
2)
Iteration 0: imputed data contain missing values
This may occur when imputation variables are used as independent
variables, when independent variables contain missing values, or when
variance-covariance matrix becomes not positive definite. You can
specify option force if you wish to proceed anyway.
3)
Iteration 0: variance-covariance matrix (Sigma) is not positive definite
EM did not converge