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RE: st: joint hypothesis test with one sided alternatives


From   "Andreas Drichoutis" <[email protected]>
To   <[email protected]>
Subject   RE: st: joint hypothesis test with one sided alternatives
Date   Wed, 9 Mar 2011 20:04:13 +0200

Can you be more specific? Why do I need to look at the inequalities
separately? Is a separate test valid?

I also tried bootstrapping: 	bootstrap ((_b[a1]<0)&(_b[a2]>0)): intreg
left right x1 x2 

But I get the below output:

----------------------------------------------------------------------------
--
             |   Observed   Bootstrap                         Normal-based
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
       _bs_1 |  (dropped)
----------------------------------------------------------------------------
--



Andreas Drichoutis


-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Nick Cox
Sent: Wednesday, March 09, 2011 7:56 PM
To: '[email protected]'
Subject: RE: st: joint hypothesis test with one sided alternatives

Seems that you could (should?) bootstrap the fraction of times your
inequalities are both satisfied. 

Note that if a1 < 0 and a2 > 0 then a1 * a2 < 0 so you need to look at the
inequalities separately. 

Nick 
[email protected] 

Andreas Drichoutis

Thanks for the reply.

test does not allow to test inequalities i.e., it only displays two-sided
tests.

Any other solutions?
  
Muyang Zhang

They are not equivalent. You can simply use -test- with multiple
equations/inequalities.

2011/3/9 Andreas Drichoutis <[email protected]>:

> I estimate an interval regression model of the form Y=_cons+a1*X1+a2*X2 .
>
> How would I run a joint hypothesis test with one sided alternatives e.g.
> that a1>0 & a2<0.
>
> Is this equivalent to testing a1*a2<0 ? Is it valid to use?:
> testnl _b[a1]*_b[a2]=0
> local sign_test=sign(_b[a1]*_b[a2])
> di _b[a1]*_b[a2] "   H0: coef<0 p-value= "
> 1-normal(`sign_test'*sqrt(r(chi2)))

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