Amelia II doesn't run this model, but you can come close for some purposes by stacking the surveys up and leaving missing entire variables not asked in any given survey.  If the surveys are related, such as sorted by time, you can use the tools in Amelia II to include that information.  You can also use Amelia II's priors to influence the imputations in potentially useful ways. 

Gary
---
http://gking.harvard.edu
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On 07/12/2009 01:08 PM, Victor Mauricio Herrera wrote: 
The actual reference to the paper I mentioned in my previous email is: 

Andrew Gelman; Gary King; Chuanhai Liu. Not Asked and Not Answered: Multiple Imputation for Multiple Surveys. Journal of the American Statistical Association, Vol. 93, No. 443. (Sep., 1998), pp. 846-857.

Thanks,

Victor Herrera MD, MSc.


Subject:
[amelia] MI: Multiple surveys
From:
Victor Mauricio Herrera <vherrera@wisc.edu>
Date:
Sun, 12 Jul 2009 12:05:03 -0500
To:
amelia lists <amelia@lists.gking.harvard.edu>
To:
amelia lists <amelia@lists.gking.harvard.edu>		 

Hello Amelia users:

I am working with a pool of surveys and I want to impute missing values in the pooled dataset while keeping the design variables and re-calculated weights (and the variables from which those weights were derived). From the paper by King & Liu (1998) on multiple imputation for multiple surveys now I know that a hierarchical approach to this problem is the appropriate one; however, after reading the documentation of the software (Amelia II) I am not sure whether this task can be accomplished. 

I will appreciate your help on this issue.
Thanks,
 
Victor Herrera MD. MSc.
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