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


From   Stas Kolenikov <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: oheckman w/twostep - can I bootstrap for a Wald?
Date   Tue, 9 Jul 2013 10:17:20 -0500

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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