Thank you for the reply Spencer.  GMM does indeed have "L-BFGS-B" as a
method, I and I believe m(q) can be expressed as mq with m as a square
matrix.  I will give this a try and post the results to the list.

On Wed, Jul 22, 2009 at 11:40 AM, spencerg <spencer.graves@prodsyse.com>wrote:

>     I haven't used "gmm", but if it will pass 'method = "L-BFGS-B"' with
> "lower = 0", you might be able to transform your problem into that form as
> follows:
>
>     Let m(q) = m q, where m = an n x p matrix, and q = a p x 1 vector.  If
> n = p, then let z = m q so q = solve(m, z), and parameterize your function
> "g" in terms of z.
>
>     If n < p, first compute the qr decomposition of t(m)
>
>
> m <- matrix(1:2, 1)
> mq <- t(qr.Q(qr(t(m)), complete=TRUE))
>
>
>     Then let mc be an n x n matrix with the first n rows = m and the last
> (p-n) rows being the last (p-n) rows of mq.  Then let z = mc q and solve
> similar to the above.
>
>     If n > p, I suggest you start by solving the unconstrained problem,
> then find out which constraints are violated, add the constraints that seem
> to be violated the worst perhaps one at a time iteratively until you have a
> solution.
>
>     Or you could read the code for "gmm" and try to modify it to use
> "constrOptim".
>
>     Hope this helps.     Spencer Graves
>
>
> David J. Moore, Ph.D. wrote:
>
>> I need m(theta) > 0 where m() is a linear function of theta.  While the
>> reparameterization is a creative workaround for a parameter constraint,I
>> do
>> not believe I can apply it here.  Other thoughts?
>>
>> David J. Moore, Ph.D.
>> Visiting Assistant Professor of Finance
>> The University of Memphis
>>
>>
>> On Jul 18, 2009, at 2:04 PM, Liviu Andronic <landronimirc@gmail.com>
>> wrote:
>>
>>  From: Eric Zivot <ezivot@u.washington.edu>
>>
>>
>>> Date: Sat, Jul 18, 2009 at 8:08 PM
>>> Subject: Re: [R-SIG-Finance] Inequality constraints in GMM estimation?
>>> To: Liviu Andronic <landronimirc@gmail.com>
>>>
>>>
>>> Not sure your inequality constraint makes sense. Do you want theta > 0
>>> or m(theta) > 0
>>>
>>> Usually, inequality constraints are put on parameters. If you want to
>>> constrain theta > 0 then just re-parameterize theta as theta =
>>> exp(gamma) and optimize over gamma. You will need to use delta method
>>> to get the correct std errors for theta.hat = exp(gamma.hat).
>>>
>>> ****************************************************************
>>> *  Eric Zivot                                                  *
>>> *  Professor and Gary Waterman Distinguished Scholar           *
>>> *  Department of Economics                                     *
>>> *  Adjunct Professor of Finance                                *
>>> *  Adjunct Professor of Statistics
>>> *  Box 353330                  email:  ezivot@u.washington.edu *
>>> *  University of Washington    phone:  206-543-6715            *
>>> *  Seattle, WA 98195-3330                                      *
>>>                                                  *
>>> *  www:  http://faculty.washington.edu/ezivot                  *
>>> ****************************************************************
>>>
>>> On Sat, 18 Jul 2009, Liviu Andronic wrote:
>>>
>>>  Hello,
>>>
>>>
>>>> On Sat, Jul 18, 2009 at 4:58 AM, David J. Moore, Ph.D.<djmphd@gmail.com
>>>> >
>>>> wrote:
>>>>
>>>>
>>>>
>>>>> I have a relatively simple finance application of GMM. Given the moment
>>>>> condition:
>>>>> E[m*R]=0
>>>>>
>>>>> where m=m[theta]
>>>>>
>>>>> I would like to constrain m>0. Any ideas?
>>>>>
>>>>>  Check library(gmm).
>>>>>
>>>>>
>>>> Liviu
>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>>
>>>
>>
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>>
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>>
>
>

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