Hi Jesus,
We'll take a look at the error in overimpute. Thanks for pointing it out.
As for loess, we don't offer it in Amelia currently, but you can always
create the basis functions yourself and then include them as additional
covariates in the imputation model. That shoul achieve the results you
desire.
Cheers,
Matt
On Thu, Sep 25, 2014 at 9:22 AM, Jesus Borruel <oieregi(a)gmail.com> wrote:
To all users of Amelia,
Using basis functions in case of time series cross section data seems to
improve amelia imputations substancially (at least in my datacase).
**In Amelia manual on define polynomials used as b0 + b1* t + b1* t^2 +
b1*t^3. I understand it is a typographic error and coefficients are
different (b0, b1, b2, b3..)
Spline function looks like improves slightly imputations (so far
splinetime=4) and it´s generally preferred for interpolation (wikipedia).
I`m just testing.
But LOESS or local regression, where on make a different regression for
each element based on the k-neighbors, using least squares regression over
a polynomial is really appealing. The tecnique is not widely used (I guess)
because until a few years ago computers didn´t allow for it.
James Honaker/Gary King achieve better imputations in "What to Do About
Missing Values in TSCS Data" with LOESS.
Meanwhile, and as far as I keep testing I have also changed from limit the
draws (, emburn=c(20,100)) to increase tolerance (,tolerance=0,001) and
results should now be more solids.
** Also trying to subset overimpute (a.out, subset=country=="france",
"Vtas") gives me this error:
Error in `[<-`(`*tmp*`, , j - 1, value = c(NA, NA, NA, NA, NA, NA, NA, :
subscript out of bounds
I think it could be for having variables (defined as "idvars" at imputation but
not at overimpute instruction) with "NA".
Someone has experience in changing basis functions or could give some help?
Jesus Borruel
PHD student
San Pablo-CEU University
Spain(EU)
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