Good evening,
I am new to Amelia and have been reading the related papers (King et al.
2001, Honaker and King 2008, and Honaker and King 2007)and the manual, but I
still have three doubts and I would appreciate your help or perhaps you
could suggest some more readings about how to deal with these practical
issues
*1)* In the APSR article by King et al (2001) it says: "The prediction
required is not causal [...]. To an extent then, the analyst, rather than
the world that generates the data, controls the degree to which the MAR
assumption fits. It can be made to fit the data by including more variables
in the imputation process to predict the pattern of missingness."
*Should this be taken to mean that the more variables included in the
imputation model (even if they are not very relevant for the estimation
model), the better? If this is right, I assume that such variables should
have no missing obs. (otherwise it'd be like demanding more from Amelia in
terms of imputation rather than improving the imputation), is this right?*
At some other point in the text, the authors (or this is what I
interpreted) hint that "overcontrolling" is not a problem in the imputation
model. However, I have TSCS data (700 obs.) and *when I ran AmeliaII, I
had to drop many variables (even some interaction terms that are relevant
for my analysis model) and the dummies for countries because I kept on
getting an error message reading more or less: "the number of parameters
to estimate is too large relative to observations" and another message about
multicollinearity*. So, I ended up with fewer variables in my imputation
model than in my analsysi model, which leads to my 2 question:
*2)* On p. 57 the authros mention that the imputation model should contain
at least as much information as the analysis model. Does this means that
*if I am using fixed effects in the analysis model the country dummies
cannot be excluded from the imputation model?*
*3)* Finally, I have read about the *polynomials of time* option in the
manual but I am a litte confused...Is the option polynomials of time related
to the *lags* (if I tick "1" in the polynomials box would this be
equivalent to L1 and if I tick "2" would this be equivalent to L2...,
etc.? Is it used for the lags only when the polynomials are interacted with
the cross-section?... Or are they completely different options. IF the
polynomials of time refer to "*trends*" I am wondering if it would be
redundant to include in the imputation model a variable that belongs in my
analysis model and which is a time-trend varaible.
Sorry about so many questions, but I have been trying to solve this issues
on my own and with the readings but I am unsure as to whether what I am
doing is OK.
Thank you so much.
Sincerely,
Helen A Brown