----- Original Message -----
From: Stas Kolenikov <[email protected]>
Date: Tuesday, December 7, 2004 2:10 pm
Subject: Re: st: ATE in stata 7, matching
> I'd say in this case you have a continuous variable of interest (GDP
> per capita and its growth), so to me there is no point in categorizing
> them that way: you impose some rather artificial criteria of the
> subgroup selection, then lose some of the information, and further you
> are trying to recover that lost information by introducing covariates.
> That's weird. The fact that everybody is talking about ATEs and
> excited about them does not mean that it should be applied to each and
> every problem.
>
In addition to Stas' comments, partitioning the dependent variable,
GDP growth rate, into fast and slow growing countries could produce
incorrect results. Koenker and Hallock (2001 "Quantile Regression"
Journal of Economic Perspectives, 15:4 pages 143 -156) refer to this as
"truncation on the dependent variable." This method of truncation is
vulnerable to selection bias, since one is truncating the full sample
based on the dependent variable in the model. This can produce both
biased and inconsistent estimates.
As Koenker and Hallock make clear, a more appropriate econometric technique is
quantile regression (-qreg-). With quantile regression, one can focus on the
conditional distribution of the dependent variable and avoid the selection bias
associated with truncated regression. This is because with a quantile
regression, one can choose the central tendency point around which to estimate a
regression - for example, 10th decile rather than the mean - without truncating
the sample to exclude the upper 90 percent of data.
In addition to the Koenker and Hallock article, you might find useful:
Giorgio Canarella and Stephen Pollard "Parameter Heterogeneity in the Neoclassical
Growth Model: A Quantile Regression Approach" Journal of Economic Development.
Volume 29, Number 1, June 2004.
Hope this helps,
Scott
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