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

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

Hi Natasha:

Actually, you can have your random-effects cake and eat it too with a 
fixed-effects cherry on the top.  See:

Mundlak, Y. (1978). Pooling of Time-Series and Cross-Section Data. 
Econometrica, 46(1), 69-85.

That is, suppose that x is the region varying covariate that you wish to 
estimate.

Taking the cluster mean of x and including as a covariate in the 
regression then essentially includes the fixed-effect. This procedure is 
nicely explained here:

Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and Longitudinal 
Modeling Using Stata. College Station, TX: Stata Press.

I have an in-press paper where we explain it too (if you e-mail me 
off-line, I will send it to you as well as to anyone else who'd like copy):

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (in press). On 
making causal claims: A review and recommendations. The Leadership 
Quarterly, 21(6).

Not including the fixed-effect would be a very fatal flaw to make; this 
obsession in econometrics is a well founded one because if the 
assumptions of the random effects estimator are not met (with the 
random-effects being orthogonal to the regressors) then the estimator is 
not consistent. More fields are paying attention to this matter, 
including sociology, and applied psychology/management. For further 
discussion see the following as well:

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

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
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 06.06.2010 13:56, Maarten buis wrote:
> --- On Sun, 6/6/10, natasha agarwal wrote:
>   
> > I estimate it with a within estimator, i.e. ur fixed
> > effects. When I do so the minute I include all the time 
> > dummies, FDI goes insig but thats my main variable of
> > interest. The problem that I have found out is that FDI
> > is behaving like a time dummy because all the firms
> > within a region for each year will have the same value
> > of FDI. 
> >     
>
> The problem with macro quantities is that they are usually highly 
> correlated with other macro quantities and time, so it is hard to
> disentangle the effects. Moreover, there are only so many 
> observations possible on the macro level. These two together means
> that often you have very little statistical power to play with. 
>
> The advantage of fixed effects regression is that it provides a
> strong reason for believing that you controlled for some of the 
> unobserved and possible confounding variables on the the region 
> level. However, the price you have to pay is that you loose a lot 
> of power; i.e. you loose all the information from the comparison 
> between regions. Since you don't have a lot to begin with, that 
> is very bad news.
>
> Random effects don't allow you to controll for unobserved 
> confounding variables on the region level (the random effects are
> assumed to be uncorrelated with the observed variables), but it
> does allow you to also use the information from the comparison
> between regions.
>
> Then you have rather pragmatic discipline specific reasons: e.g.
> within economics they tend to be so obsessed with fixed effects
> regression, that you will have a hard time getting a random 
> effects model published.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>       
>
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