I am trying to impute a data set containing panel data (country-years) on socio-economic indicators for developing countries. I have troubles with a one-week limit on our cluster service and look for options to speed things up. 

The overall missingness in the dataset is 40%. Some variables have missingness over 60% (see below missingness per variable in one of my data sets).

Here’s my amelia specifications (using Amelia 1.7.2 and R 3.1.3)

I created about 150 single jobs (m=1) with different variables included in the imputation (e.g. leaving out a high-missingness variable in one of those versions;leaving out years that have high missingness). I wanted to run all these 150 imputations on a cluster, but none of these converge before the cut-off limit of one week.

I wonder if I can do anything else other than reducing missingness in the original data and submitting parallel jobs to a cluster service. 

I did think about requesting more memory for each job, but I am not sure this is likely to work, since the automatic script for memory allocations tends to be good at assigning as much memory as needed.

I saw on this help list:

"The "empri" argument sets an empirical/ridge prior.  A value of a half to 1 percent of the sample size would be small, aid numerical stability, and unlikely to noticably change results (unless you are using time series cross sectional data, in which case you might use 1 percent of the sample within any cross sectional unit).

Unfortunately I don’t quite understand these options, and I’m not sure on how to go about testing which of these options speed up Amelia, and how ‘far’ I can go not to mess things up.

If you have any tips for me on what my next steps should be I’d be very grateful!

Best,

Nicole

P.S. If anyone wants to see the data to help please let me know and I’d be happy to send them over.

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