Sir,
I use try and error method to improve imputations with a database of firm's
variables (time series cross section data with collinearity:1800
rows-observations, 163 firms, 43 column-variables,) .
It has been helpful to limit number of draws to 100 (,emburn=c(20,100))
after getting convergence with 400 and 500 draws and seen disperse grafics
(a.out, dims=1,m=5) visually converge at draws 70-80.
Also taking out variables for collinearity, adding lags and leads and using
polynomials (polytime =3, much better than polytime =2 or not polytime at
all).
My problem is I'm not able to know how use LOESS smoothing to create basis
functions. From "What to do about missing values in TSCS data":*
(Finally,we also ran a third set of 120 imputation models,this time using
LOESS smoothing to create the basis functions... LOESS based smoothing
provides a clear advantage over polynomial smoothing: almost as many points
are captured by the 90% confidence intervals as for the polynomials, but
the LOESS-based intervals are narrower in almost all cases, especially when
the polynomial-based intervals are largest.)*
I´ve read carefully Amelia II, What to do with TSCS data and also posting
lists in Amelia archives but got no conclusion.
I'd appreciate any help any hint
Jesus Borruel
PH student
San Pablo-CEU University
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