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