Re: st: RE: How to perform a Wald test between the fixed effect...

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

Hi Frank:

See the following, with the basic explanations about the Mundlak procedure:

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On 
making causal claims: A review and recommendations. The Leadership 
Quarterly, 21(6). 1086-1120. 
http://www.hec.unil.ch/jantonakis/Causal_Claims.pdf
(refer to the paper by Mundlak for technical explanations)

Try this (suppose you want to test for race):

clear
webuse nlswork
xtset idcode
xtreg ln_w race age , fe r
est store fe

*notice race gets kicked out

bys idcode: egen mean_race = mean(race)

xtreg ln_w race age , re r
est store re

est tab fe re, se t

So, you can have your cake an eat it too. A fixed-effects estimator with 
time-invariant predictors.

So, basically, if you include the cluster means of all time-varying 
variables, and assuming that ff is exogenous, then you can interpret the 
coefficient of ff.

HTH,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________

On 20.02.2013 17:57, Jacobs, David wrote:
> You might find Paul Allison's book in the paperback Sage series on methods (it has fixed-effects in the title) to be quite useful.  In it Allison outlines a way to estimate time-variant explanatory variables with fixed-effects combined with (or included in the same model) a random-effects estimate of one or more time-invariant explanatory variables.  Of course, the estimate of the coefficient on the time-invariant explanatory variable may suffer from omitted variable disturbances due to the absence of the automatic controls in the non-fixed-effects estimate, but a solution like this may be your only choice.
>
> I understand that I am not answering your exact question, but this quite different approach may nevertheless suffice.
>
> Dave Jacobs
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Frank Barbera
> Sent: Wednesday, February 20, 2013 4:59 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: How to perform a Wald test between the fixed effect...
>
> Dear Stata users, I'm attempting to perform what appears to be a relatively simple procedure in Sata, but due to my lack of experience with the program, I'm having a really hard time. I'm using a panel to estimate a the following standard fixed effects model
>
> (1)	yit = ai + bxit + eit.
>
> Where i are individual firms (3450 firms, coded as 'id') and t is time (3 years, coded as 'time'). Each firm can further be classified as belonging to a group (called 'ff') or not (I'll call these 'not ff'). This is done by way of a dummy variable equating to 1 if the firm is in the ff group and 0 otherwise. The problem is that ff is time invariant, so its effect cannot be directly estimated in the model (it's been absorbed into ai). Of course a random effect specification would allow me to observe the ff effect on y, but the data violate the assumption of no correlation between the unique errors and the regressors, so the fixed effect model is preferred. I took a closer look at the estimates for ai by way of the following command
>
> regress y x i.id, noconstant
>
> and simply compared the average ai for firms in the ff category with those in the not ff category. I now wish to officially test if the average ai (for ff firms) = average ai (for not ff firms), or in other words if the ff effect is significant. A Wald coefficient test with the restriction that average ai (for ff firms) - average ai (for not ff firms) = 0 should do it, but since I estimated (1) using the i.id option, I have no idea how to do this within (1). i.e. I do not wish to perform a fixed effect vector decomposition in multiple stages.
>
> Can anyone help?
>
> Frank Barbera
>
>
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