<suest> followed by <nlcom> might be relevant.
One way of storing estimation results is to use <estimates store>. The
help file for <nlcom> contains an example.
The alternative is to write a program that stores estimation results in
matrices, and then passes them to your own code for the test.
Jean Salvati
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Roger Newson
> Sent: Thursday, November 17, 2005 5:13 PM
> To: [email protected]
> Subject: Re: st: An urgent request for help :-)
>
> You don't state whether these 2 regressions are estimated on
> the same or different sets of data. If they are estimated on
> the same set of data, then any solution will probably use
> -suest- to create a combined covariance matrix for the 2
> regressions. If they are from independent sets of data, then
> the 2 covariance matrices (extracted from the estimation
> results) can be combined diagonally to produce a combined
> covariance matrix. Once this combined covariance matrix is
> produced, you can produce a row vector of the derivatives of
>
> G=(B01-B02)/(B11-B12)
>
> with respect to B01, B11, B02 and B12, and use this vector,
> and the combined covariance matrix, to compute a covariance for G.
>
> I don't know how much you know about -suest-, Stata matrices,
> and extracting estimation results. However, -whelp matrix-
> will introduce you to Stata matrices, -whelp suest- will
> introduce you to -suest-, and -whelp
> estimates- will introduce you to estimation results.
>
> Best wishes
>
> Roger
>
>
> At 20:47 17/11/2005, you wrote:
> >Hello,
> >This may be a slightly basic question, but I would really
> appreciate a
> >solution... I have two regression estimations which I run
> sequentially,
> >and need to get a 95% confidence interval using coefficients
> from both
> >these estimations in a non-linear combination.
> >Specifically, I want to get the (delta-method estimated) 95% conf.
> >interval for the following expression:
> >
> >(B01 - B02)/(B11-B12),
> >
> > where B01 is the intercept of the 1st regression, B02 is the
> >intercept of the second equation, B11 is a predictor
> coefficient of the
> >1st equation, and B12 is a predictor coefficient of the 2nd equation.
> >
> >The models I'm running are univariate regressions, but I'd
> appreciate a
> >multi-variate generalization as well. My question centres around how
> >one can store and recall estimations from previous regressions while
> >trying to work with a non-linear combination of their coefficients.
> >
> >Thank you, and looking forward to hearing from you,
> >
> >Sincerely,
> >
> >Manasi
> >*
> >* For searches and help try:
> >* http://www.stata.com/support/faqs/res/findit.html
> >* http://www.stata.com/support/statalist/faq
> >* http://www.ats.ucla.edu/stat/stata/
>
>
> --
> Roger Newson
> Lecturer in Medical Statistics
> Department of Public Health Sciences
> Division of Asthma, Allergy and Lung Biology King's College London
>
> 5th Floor, Capital House
> 42 Weston Street
> London SE1 3QD
> United Kingdom
>
> Tel: 020 7848 6648 International +44 20 7848 6648
> Fax: 020 7848 6620 International +44 20 7848 6620
> or 020 7848 6605 International +44 20 7848 6605
> Email: [email protected]
> Website: http://phs.kcl.ac.uk/rogernewson/
>
> Opinions expressed are those of the author, not the institution.
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
