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

From   John Antonakis <>
Subject   Re: st: Estimating firm level data on regional level data using a within estimator.
Date   Sun, 06 Jun 2010 15:44:30 +0200

Hi Maarten:

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?

Again, as I said below, one can combine a model where fixed effects are partialled-out and where level 2 (e.g., firm-level) varying effects can be included. I cannot possibly understand why someone would knowingly not use this approach if they knew that their estimator is not consistent yet still wanted to include Level 2 effects. Finally, in the medical science they are rightly concerned with knowing whether a treatment is efficacious or not, or whether it is toxic or not; lives are at stake here so we cannot take guesses as to what the coefficient may be. The gold standard is the randomized field experiment and rightly so. There are other methods available to recover causal estimates and they are used a lot in the medical sciences (e.g., propensity scores). Same with economics: Te Heckman two-step model or regression discontinuity can be good alternatives to randomized experiments. These alternative procedures are accepted and used by many researchers, so I would not characterize economists or medical researchers as being fixated by a specific method.

Given you are from sociology and if you have a moment, take a look at the Halaby paper:

Halaby, C. N. (2004). Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30, 507-544.

It talks about the fixed-effect problem nicely as well as "tricks" to overcome endogeneity using mixed-estimator approaches.



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

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:

Personal page:

On 06.06.2010 14:45, Maarten buis wrote:
--- On Sun, 6/6/10, John Antonakis wrote:
Not including the fixed-effect would be a very fatal flaw
to make

The assumption is no stronger than the one we make any time we type -regress-. I see the strenghts of fixed effects regression, but it is not the solution to all problems that
some people believe it is. So to call not using fixed effects
regression a "fatal flaw", is way too strong for my taste. A similar fixation sometimes occurs within medical sciences, who sometimes refuses to look at any estimate that isn't generated with a randomized experiment. The problem is that
in these cases the method becomes a more important part of
the argument than the data, thus forgetting what empirical
science is all about.

-- Maarten

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen

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