Thanks for the help! I have tried running the model
using both methods suggested in the email below by
using the 2 different ways to call the model
~ svrest "ml_try2", mat(e(b))
~ svrest "ml model lf mlcot (price=mpg weight)/sigma,
robust search(quietly) max", mat(e(b))
And I got both methods to run & produce results.
Now here's my next dilemma...should the standard
errors be the same, regardless of how I call the
model? The parameter estimates from these 2 methods
are the same. However, I got very different standard
errors when using these 2 different ways of calling
the model. (8.21e-07 vs. .0484)
I also tried using -nl- with SVR, since the model is
non-linear in the parameters. (I had been using -ml-
per a posting I had found in the FAQs on how to
estimate a nonlinear model using -ml-)
To try & see if using SVR would work with the -nl-
model, I used the 2nd method to call the nonlinear
model
~svrest "nl cot logcot", mat(e(b))
and I got results that were the same (both in the
parameters & the standard errors) as when I called the
-ml- model using the 2nd method [i.e. svrest "ml model
lf mlcot (price=mpg weight)/sigma, robust
search(quietly) max", mat(e(b))]
(I hope my way of explaining things is making sense!)
So my questions are: why do the ways of calling the
-ml- model produce such different results?
and which one is the better way to get the results?
Thanks.
-Kat
--- Nicholas Winter <[email protected]> wrote:
> You need to set up two programs: -svrest- needs you
> to call a program to
> estimate your model. In turn, -ml- needs you to
> call a program to
> calculate the likelihood.
>
> So here's one approach (I've switched the model to
> OLS for this example;
> you should get your interactive ML program to work
> without worrying about
> the jackknife; then insert it in mlcot.ado):
>
> *------------- begin ml_try2.ado -----------------
> program define ml_try2
> version 8.1
> ml model lf mlcot (price = mpg weight) /sigma
> ml search
> ml maximize
> end
> *------------- end ml_try2.ado -------------------
>
> *------------- begin mlcot.ado -----------------
> program define mlcot
> version 8.1
> args lnf xb sigma
> qui replace `lnf'
>
=-ln(sqrt(2*_pi*`sigma'^2))-1/(2*`sigma'^2)*($ML_y1-`xb')^2
> end
> *------------- end mlcot.ado -------------------
>
> THEN, you can submit your command to stata:
>
> . svrest "ml_try2" , mat(e(b))
>
>
> However, there is a slightly easier way. Because
> the -ml- command lets you
> specify your model and maximize it in one command
> (ie, in non-interactive
> mode), you could do this:
>
> (1) define mlcot.ado as above
> (2) submit this single command:
>
> . svrest "ml model lf mlcot (price = mpg
> weight) /sigma , robust
> search(quietly) max" , mat(e(b))
>
>
> See -help ml- if this last doesn't make sense.
>
> Cheers,
> Nick Winter
>
>
> >I want to use an ado file to work with -svrest-,
> >however I'm running into problems when it gets to
> the
> >ado file trying to run.
> >
> >I've been submitting the -svrset- part first. I
> then
> >try to submit the line
> >svrest "ml_try2", mat(e(b))
> >and here is where I run into problems.
> >
> >Below you will find the code in my ado file.
> >--------- begin ml_try2.ado ----------------
> > program define mlcot
> > version 8.1
> > args lnf B0 B1 B2 sigma
> > tempvar res
> > quietly gen double `res' = logcot -
> >(`B0'-`B1'*(exp(-`B2'*smd720)))
> > quietly replace `lnf'=-0.5*ln(2*_pi)
> -ln(`sigma')
> >-0.5*`res'^2/`sigma'^2
> > end
> > ml model lf mlcot (B0:) (B1:) (B2:) (sigma:),
> robust
> > ml search
> > ml maximize
> >--------- end ml_try2.ado -----------------
> >
> >I get the following error:
> > unrecognized command: mlcot
> > program mlcot not found
> > error when command executed on original dataset
> > invalid syntax
> >
>
>
--------------------------------------------------------
> Nicholas Winter 308 White
> Hall
> Assistant Professor
> t:607.255.8819
> Department of Government
> f:607.255.4530
> Cornell University
> [email protected]
> Ithaca, NY 14853-7901
> falcon.arts.cornell.edu/nw53
>
> *
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> *
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