[amelia] Can Amelia do MI if all variables have missing data? (Stata can't)

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