Hi all,
This is not a programming question but one on theory. I would be grateful for any views about this.
I am trying to decompose income inequality using DiNardo, Fortin and Lemieux's (1996) semi-parametric methodology (Econometrica 64(5), 1001-44) as applied to the kernel density function.
However, DiNardo et al, Deaton (Analysis of Household Surveys - 1997) and D'ambrosio (Review of Income and Wealth 47(1), 2001) transform the data into log-form and work with that.
This is because the kernel estimator has difficulties handling densities that have a high degree of asymmetry as is the case with income data. Densities close to normality are the easiest for the kernel estimator to estimate.
Now my question is this:
Would I be able to get round this problem of the kernel not performing well if I were to estimate adaptive kernel density (akdensity) on the original data?
The adaptive kernel density curve, though smoother than the kernel density function, is also skewed or asymmetric. Wouldn't this matter?
Would someone know of a useful reference in this regard?
Thanks so much.
Ramani
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