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


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: How to perform a Wald test between the fixed effect...
Date   Thu, 21 Feb 2013 10:02:57 +0100

Hi Frank.

Can you use the nlswork dataset shipped with Stata and work with the example I gave you? I am not sure what it is you want to do any more. I thought you wanted to see whether a time invariant predictor was significant. That is the test that the estimate (in my example of race) is not equal to zero....that is the test you want which for 1 parameter is equal to a Wald test, e.g., by using -test race-

eg..,

clear
webuse nlswork
xtset idcode
xtreg ln_w race age , fe r
bys idcode: egen mean_race = mean(race)
xtreg ln_w race age , re r
test race
dis sqrt(r(chi2))

Which equals the z value, 7.37, from the model.

Best,
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 21.02.2013 08:29, Frank Barbera wrote:
> First off, I'd like to thank Dave and John for their responses.
>
> John, your work on this issue has been very helpful in particular. And (when there's time) I'll be sure to read the Allison book which Dave has mentioned. With that said, I have already considered Mundlak's approach, but the issue of performing the Wald test within the fixed set-up remains (as this will be compared to the random and Mundlak estimates).
>
> With respect to my previous message, I've been playing with a toy dataset and noticed that if I -generate- the following id variables...
>
>     generate ffid = id*ff
>
>     generate nfid = id*(1-ff)
>
> which separates the id into two groups (ff and nf). To estimate the ai's...
>
>     xi: regress y x i.ffid i.nfid, noconstant
>
> which, unbeknownst to me, creates a dummy for each firm (coded as _Iffid_i, where i = {1...10} in my toy dataset. The fist 5 blong to ff while the last 5 belong to nf). Then for the restriction, Wald test of the average ai ff = average ai nf by...
>
> test ( _Iffid_1+_Iffid_2+ _Iffid_3+_Iffid_4+ _Iffid_5)/5 = (_Infid_6+ _Infid_7+_Infid_8+_Infid_9+_Infid_10)/5
>
>     F(  1,     9) =    0.38
>                 Prob > F =    0.5532
>
> which seems to have worked (albiet there is no ff effect). Can anyone confirm if this procedure is correct? If so, how to do the final step for 3450 dummies?
>
> Sorry for the basic questions, but I'm completely new to Stata.
>
> Frank Barbera
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of John Antonakis
> Sent: Thursday, 21 February 2013 3:20 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: RE: How to perform a Wald test between the fixed effect...
>
> 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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