Re: st: AW: Re: AW: treatment effect estimation with an ordinal 1st step and a continuous 2nd step

[Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>]

No, you wouldn't. It is just something I find tends to break Mata
functions, that they are not properly indexed at startup.

But obviously in this case that is not the problem. I can confirm that
the latest ado produces the error reported.

T

2010/7/18 Martin Weiss <martin.weiss1@gmx.de>:
>
> <>
>
> " mata: mata mlib index"
>
>
>
> Doesn`t do any good for me. I have
>
>
> . which cmp
> c:\ado\plus\c\cmp.ado
> *! cmp 3.6.0 5 July 2010
> *! David Roodman, Center for Global Development, Washington, DC, www.cgdev.org
> *! Copyright David Roodman 2007-10. May be distributed free.
>
>
> Anyway, would I be able to tell from the help file that I need to -mata: mata mlib index-???
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Tirthankar Chakravarty
> Gesendet: Samstag, 17. Juli 2010 22:46
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: AW: Re: AW: treatment effect estimation with an ordinal 1st step and a continuous 2nd step
>
> Martin,
>
> That example runs without problems. You might want to try
>
> mata: mata mlib index
>
> before you run that example.
>
> T
>
>
> 2010/7/18 Martin Weiss <martin.weiss1@gmx.de>:
>>
>> <>
>>
>>
>> " However, the following syntax
>>
>> xi: cmp (y1  = x1) (y2 = x2 y1), ind(5 1)"
>>
>>
>>
>> What is -xi- good for in your -cmp- call, btw?
>>
>>
>>
>> HTH
>> Martin
>>
>>
>> -----Ursprüngliche Nachricht-----
>> Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von ???
>> Gesendet: Samstag, 17. Juli 2010 22:17
>> An: "Martin Weiss"
>> Cc: statalist@hsphsun2.harvard.edu
>> Betreff: st: Re: AW: treatment effect estimation with an ordinal 1st step and a continuous 2nd step
>>
>> Dear Martin,
>>
>> Thanks for your comment.
>> It was helpful.
>>
>> However, the following syntax
>>
>> xi: cmp (y1  = x1) (y2 = x2 y1), ind(5 1)
>>
>> where y1 = ordinal numbers like 1, 2, 3, 4
>>          y2 = continuous
>>
>> gives an error sign  like "matrix___00000B not found"
>>
>> And the program shows (y1 = x1) result only, which is exactly identical to
>> the outcome from an (oprobit y1 x1) regression.
>>
>> Thanks in advance.
>>
>> Jaemin
>>
>>
>>
>>
>>
>> ----- Original Message -----
>> From: "Martin Weiss" <martin.weiss1@gmx.de>
>> To: <statalist@hsphsun2.harvard.edu>
>> Sent: Saturday, July 17, 2010 10:45 PM
>> Subject: st: AW: treatment effect estimation with an ordinal 1st step and a
>> continuous 2nd step
>>
>>
>>>
>>> <>
>>>
>>> Try
>>>
>>> *************
>>> ssc d cmp
>>> *************
>>>
>>>
>>>
>>> HTH
>>> Martin
>>>
>>> -----Ursprüngliche Nachricht-----
>>> Von: owner-statalist@hsphsun2.harvard.edu
>>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von ???
>>> Gesendet: Samstag, 17. Juli 2010 15:29
>>> An: statalist@hsphsun2.harvard.edu
>>> Betreff: st: treatment effect estimation with an ordinal 1st step and a
>>> continuous 2nd step
>>>
>>> Hi all!
>>>
>>> I want to run a treatment effects model.
>>>
>>> In my case, the 1st step dependent var. is ordinal (for exmaple,
>>> "perfectly
>>> not matched", "not matched", "matched", and "perfectly matched"), and the
>>> 2nd step dependent var. is continuous (for example, wage in log).
>>> I have run the similar model using "treatreg" command if the 1st step
>>> depednat is binary.
>>>
>>> Is there any command working in this case.
>>>
>>> I found "mtreatreg" is available if
>>> (1) 1st step dependent var. is multivariate and
>>> (2) 2nd step DV is continuous
>>>
>>> Of course, 1st step ordinal treatment variable should be shown in the 2nd
>>> step equation as an independent variable explicitly.
>>>
>>> P.S.: I don't think running "oprobit" as the 1st step, and insertting IMR
>>> from it as independent variable into the 2nd step OLS regression is valid.
>>>
>>> Thanks,
>>> jaemin
>>>
>>> *
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>>
>>
>> *
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>
>
>
> --
> To every ω-consistent recursive class κ of formulae there correspond
> recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
> belongs to Flg(κ) (where v is the free variable of r).
>
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>



-- 
To every ω-consistent recursive class κ of formulae there correspond
recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
belongs to Flg(κ) (where v is the free variable of r).

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/