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(a)bu.edu http://www.bu.edu/sthacker/