On Mon, Sep 6, 2010 at 5:54 PM, Matt Blackwell <blackwel@fas.harvard.edu> wrote:

Hi Antonio,

Bad news first. Amelia cannot handle non-ignorable missing data
situations. You would have to write a specialized model for that.

Good news. It sounds like you might have ignorable missing data. If
you believe that the missing data depends on only variables that you
observe, then the missing data is still missing at random. Thus, you
could include income and democracy as variables in the imputation
model to satisfy that assumption. Including any other variables on
which the missingness could depend will also help to satisfy MAR.

Cheers,
matt.

On Wed, Sep 1, 2010 at 7:12 PM, Antonio P. Ramos
<ramos.grad.student@gmail.com> wrote:
>> Hi all,
>>
>> I'm working with a panel data set which contains many missing values for
>> demographic variable (e.g. mortality rates, life expectancy, etc). Some of
>> them will be modeled as outcome variables. As it is well know, they are not
>> missing at random, mostly concentrating around poor and non-democratic
>> countries. Thus I am assuming I have to model the missing process or at
>> least provide some information such as the mean of the missing data should
>> lower or higher for a particular set of countries. I have seen that I can
>> provide some prior for the imputation procedure in your software but I not
>> sure whether this is an explicit model of the non-ignorable process. Any
>> suggestions?
>>
>> Congratulations on your amazing software!
>>
>> Help and advice really appreciated,
>>
>> Antonio.
>
>

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