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

 From "Jacobs, David" To "'statalist@hsphsun2.harvard.edu'" Subject st: RE: How to perform a Wald test between the fixed effect... Date Wed, 20 Feb 2013 16:57:30 +0000

```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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