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st: RE: RE: RE: Dependent variable [with zero mass point]


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: RE: Dependent variable [with zero mass point]
Date   Wed, 6 Sep 2006 11:39:23 +0100

It may be that the zeros represent a _qualitatively_ 
subset deserving separate modelling. But that would 
be substantive knowledge and isn't given here. 

I can imagine various situations in which this 
might arise: 

1. The data are changes. The zeros represent a large subset 
who didn't change over the period, or so the data say. 

2. The data are amounts in accounts. Positive: the "bank"
owes the client, negative: the client owes the "bank",
zero: neither (perhaps no transactions, etc.). 

In these examples, the zeros are definitively intermediate 
qualitatively as well as quantitatively. 

Nick 
n.j.cox@durham.ac.uk 

Feiveson, Alan
 
> I would think that you would need distinct models for the 
> probability of
> a zero and for the the conditional non-zero distribution. Perhaps
> something like -heckman- might work. Even if the zero is not 
> important,
> you can't use something like a normal distribution to model 
> the variable
> unconditionally.
 
Nick Cox
 
> Without more known about the underlying science, it is difficult to
> comment.
> 
> But one answer is that you don't necessarily need to do anything
> special. It is the conditional distribution of response given 
> predictors
> that is the stochastic side of modelling, not the unconditional
> distribution. Besides, a spike near the middle is not much of a
> pathology compared with one at an extreme. 

Francesca Gagliardi
  
> > I would be grateful if anyone could give me suggestions on 
> how to deal
> 
> > with a dependent variable that has a mass point at zero and is 
> > continuosly distributed over negative and positive values. 
> In such a 
> > case, which is the most appropriate model to estimate?

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