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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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