Hello,
Some colleagues and I are trying to use Amelia to impute missing
data in a time-series-cross-section (TSCS) dataset (what some might
call a panel or pooled dataset). We have been able to run the
imputation successfully, but we are getting somewhat odd results
within countries.
The results of our imputations produce means, standard
deviations, etc. that are pretty consistent with our original data.
That is, they seem to be pretty good "on average." But
for this particular project, we are most interested in using these
data for descriptive purposes, and especially to show trends over time
within countries and groups of countries.
The problem we are encountering is that we are getting some very
odd results within each country's trend. For example, a country
may have values that look like this in one of the imputed
datasets:
30
[20.16830738]
[47.24110787]
[-15.5455354]
[-35.74856172]
45.9
(Brackets indicate imputed values. These particular data
are for telephone mainlines in Barbados from 1960 to 1965.)
Later years show a clear increasing trend with less missing data,
but these imputations are all over the place with respect to the time
trend of the country.
Our dataset has 8 variables (plus the country and year variables)
and 7742 observations.
Here are the options we used in the imputation:
_AMcs=2 (our country
ID)
_AMts=1 (year variable)
_AMtstep=1
_AMlagvs=10 (we arbitrarily
chose one of our variables to lag)
We have tried it setting
_AMusets=1 and =0 and have gotten similar results either
way.
Does anyone have any suggestions or thoughts on this? Are
we using Amelia incorrectly? Is it perhaps the wrong tool for
this particular job?
Thanks,
Strom
--
Strom C. Thacker
Associate Professor of International Relations
Director, Latin American Studies Program
Boston University
152 Bay State Road
Boston, MA 02215
Tel: 617.353.7160
Fax: 617.353.9290
sthacker@bu.edu
http://www.bu.edu/sthacker/