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Re: st: oheckman w/twostep - can I bootstrap for a Wald?


From   Trent Spaulding <[email protected]>
To   [email protected]
Subject   Re: st: oheckman w/twostep - can I bootstrap for a Wald?
Date   Fri, 12 Jul 2013 14:06:05 -0400

Thanks Stas. I will explore -cmp-.


On Tue, Jul 9, 2013 at 11:17 AM, Stas Kolenikov <[email protected]> wrote:
> My gut feeling is that you can do this whole thing using David
> Roodman's award winning -cmp-, at least as an alternative FIML
> procedure. It has some learning curve, but once you figure it out, you
> won't run any non-standard multi-equation estimation problems other
> than through -cmp- :).
>
> -- Stas Kolenikov, PhD, PStat (SSC)
> -- Senior Survey Statistician, Abt SRBI
> -- Opinions stated in this email are mine only, and do not reflect the
> position of my employer
> -- http://stas.kolenikov.name
>
>
>
> On Tue, Jul 9, 2013 at 9:25 AM, Trent Spaulding
> <[email protected]> wrote:
>> As a reference, I built the bootstrap test this way following page 176
>> of Chiburis and Lokshin's article in the Stata Journal:
>> http://www.stata-journal.com/sjpdf.html?articlenum=st0123
>>
>> Does this appear valid or should I find a different approach?
>>
>> On Mon, Jul 8, 2013 at 4:38 PM, Trent Spaulding
>> <[email protected]> wrote:
>>> Can anyone check me on this?
>>>
>>> What I am trying to get: Estimates of the Wald or LR tests.
>>>
>>> Details:
>>> - Ordered categorical variable has five levels.
>>> - Running a similar model on several outcome variables
>>> - I have not successfully got the FIML estimation to converge, so I am
>>> using twostep
>>>
>>> Question: Is the following providing me a Wald test (or reasonable substitute)?
>>>
>>> -------CODE--------
>>>
>>> capture program drop aepost
>>> program aepost, eclass
>>> tempname bb
>>> oheckman **model specifications*** twostep
>>> matrix `bb'= e(rho)
>>> ereturn post `bb'
>>> end
>>> bootstrap _b, reps(100) nowarn: aepost
>>> test rho0=rho1=rho2=rho3=rho4=0
>>>
>>> -------OUTPUT--------
>>>
>>> Bootstrap results                               Number of obs      =      2275
>>>                                                 Replications       =       100
>>>
>>> ------------------------------------------------------------------------------
>>>              |   Observed   Bootstrap                         Normal-based
>>>              |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>>> -------------+----------------------------------------------------------------
>>>         rho0 |  -.2564272   .2127177    -1.21   0.228    -.6733463    .1604919
>>>         rho1 |  -.2837067   .1870317    -1.52   0.129     -.650282    .0828687
>>>         rho2 |   -.043747   .3578368    -0.12   0.903    -.7450943    .6576004
>>>         rho3 |   -.471063    .262215    -1.80   0.072     -.984995     .042869
>>>         rho4 |  -.6076915   .1845782    -3.29   0.001    -.9694582   -.2459249
>>> ------------------------------------------------------------------------------
>>>
>>> . test rho0=rho1=0
>>>
>>>  ( 1)  rho0 - rho1 = 0
>>>  ( 2)  rho0 = 0
>>>
>>>            chi2(  2) =    4.14
>>>          Prob > chi2 =    0.1264
>>>
>>> . test rho0=rho1=rho2=rho3=rho4=0
>>>
>>>  ( 1)  rho0 - rho1 = 0
>>>  ( 2)  rho0 - rho2 = 0
>>>  ( 3)  rho0 - rho3 = 0
>>>  ( 4)  rho0 - rho4 = 0
>>>  ( 5)  rho0 = 0
>>>
>>>            chi2(  5) =   17.02
>>>          Prob > chi2 =    0.0045
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