Re: st: Estimating firm level data on regional level data using a within estimator.

[John Antonakis <john.antonakis@unil.ch>]

No problem.....have your carthatic moment !

;-)

No offense taken.

Best,
J.

____________________________________________________

Prof. John Antonakis, Associate Dean 
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
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____________________________________________________



On 06.06.2010 16:34, Maarten buis wrote:
> --- On Sun, 6/6/10, John Antonakis wrote:
>   
> > The assumptions of the estimators must be met. If the
> > assumptions of the random effects estimator are not met, and
> > if the Hausman test shows that the estimator is not
> > consistent then the researcher has to bite the bullet! 
> > It is the same thing as estimating a regression model where
> > you know that x correlates with the disturbance and yet you
> > go ahead and estimate the model in any case. The coefficient
> > of x could be higher, lower, or of a different sign. What is
> > the use to society to report estimates that one knows to be
> > inconsistent?
> >     
>
> Inconsistent and bias critically depends on what you want
> to know: linear regression and random effects will give you a 
> consistent estimate on how the averages differ between groups.
> So a statement that estimate XYZ is inconsistent or biased is 
> meaningless unless you first specify (explicitly or implicitly)
> what it is that you exactly want to know. Fixed effects estimators
> are controlling for all unobserved variables that are constant
> on the higher level unit. However, you often do _not_ want to 
> controll for all variables, e.g. intervening variables. Since
> fixed effects indiscrimately controlls for all higher level
> variables, the fixed effects regression will be a inconsistent
> estimate for a large (probably the largest) subset of parameters
> of interest. On the other hand, fixed effects do not control for 
> all unobserved variables you might want to control for, in 
> particular those that aren't constant on the higher level. So
> again there are a large set of parameters of interest for which
> fixed effects are inconsistent. 
>
> In essence the only way to reliably controll for unobserved 
> variables is to observed them. Even a randomized experiment will 
> only work if the paremeter of interest is a linear combination 
> of means (and I am ignoring the problem of external validity). 
> All this is not to deny that randomized experiments and fixed 
> effects regression are useful tools in ones statistical 
> toolbox, but they are just that, a tool with advantages and 
> disadvantages. 
>
> I am being a bit hard, I guess this has more to do with my 
> frustration with some of the recent converts in my discipline. 
> As is often the case, the recent converts are the worst 
> fundamentalists. My impression is that in many cases where 
> people in my discipline use fixed effects regression those 
> people have no idea what they want to controll for (other 
> than that they want to control for "everything"), which to 
> me means that those estimates are exactly meaningless. 
> Anyhow, as I said, my frustration is with people who abuse
> a method they don't understand, and obviously none of this 
> applies to you. So don't take any of this personally.
>
> -- Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>       
>
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