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st: RE: estimating an asymmetrical relationship


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: estimating an asymmetrical relationship
Date   Tue, 15 Dec 2009 17:53:44 -0000

This seems completely ambiguous on whether you seek a model that treats
your variables as linked time series or you are lumping together data
without regard to time. 

Either way, I see no reason to rule out OLS from anything you say. All
sorts of models can, and indeed in practice should, be fitted using OLS
even if the relationship between response and predictors is some
distance from the simplest kind of linear form. 

Is there no economic theory that predicts the relation between growth
and unemployment? 

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

Gisella Young

This question is partly econometric and partly STATA, and I would
greatly appreciate any advice. I need to estimate a relationship which I
believe to be asymmetric - between growth as the explanatory variable of
interest and unemployment as the dependent variable. Asymmetric in the
sense that, while overall I expect a negative relationship, I expect the
magnitude of the relationship to be higher when growth is going down
than when it is going up (for reasons not necessary to go into here).
That is, I expect the coefficient on the relationship to be higher
during periods of high or increasing growth than during periods of low
or decreasing growth (I realise that high and increasing is not the same
thing but I will later figure out which). So I believe that OLS is not
appropriate as it will not pick up these differences, and what I am
really interested in investigating is whether there is asymmetry and how
strong it is. Ideally I would like to get
 different coefficients for the 2 types of periods (note that this is
not one long period and then another long period, but ups and downs). I
believe that I need a nonlinear estimation method to do this. But beyond
that I am not sure how to proceed and would be terribly grateful for any
suggestions.


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