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Re: st: What multiple regression model for extreme distributions


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: What multiple regression model for extreme distributions
Date   Tue, 2 Feb 2010 13:51:34 -0500

You have had a number of good suggestions already, but as Nick Cox
points out, the distribution of the dependent variable is not all that
relevant to what model you choose; it is the distribution of the
dependent variable conditional on explanatory variables that is
important.  Before you estimate a two-part "hurdle" or zero-inflated
model, I urge you to consider that the right set of explanatory
variables might well capture the reason for a large number of zero
outcomes (e.g. using -poisson- instead of -zip- etc.).  When it comes
to household saving (I think that is your dependent variable, not
independent), you also want to consider debt.  It may be the case that
households you are coding as zeros actually have negative saving
during the period under study.  Do you have panel data, or
cross-sectional data?  How is saving measured?

On Tue, Feb 2, 2010 at 10:09 AM, <muhammed.abdulkhalid@gmail.com> wrote:
> I have a household income survey data ( 38,000 observations), and my
> problem is doing a multiple regression on saving ( independent var) to
> ethnicity/strata/employment
> etc( dependent var).
>
> The problem is this : 70% of my observation for the value of saving is
> zero. I had recode it to 1 and log them, but the distribution is still
> extremely skewed ( mean 0.78, std dev is 2.4  min 0 max 14). The
> historgam still looks like the letter L , exteremly skewed to the
> right with  long tail.  Obviously, OLS is out, and I tried Poisson(
> glm nbinomial) but the distribution is still not distributed normally.
> The data are in order i.e no missing values etc etc. It is clean.For
> some reason, lobit would not run.

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