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From | Nilam Prasai <nilamprasai@gmail.com> |
To | Fernando Rios Avila <f.rios.a@gmail.com>, statalist@hsphsun2.harvard.edu |
Subject | Re: st: Estimating linear regression model by maximum likelihood |
Date | Thu, 10 Mar 2011 15:18:19 -0500 |
Dear Fernando Thank you very much. Actually I am learning stata. I am not familiar with ML function in stata. Your program helped me a lot. But I don't understand what you mean by "the parameters a1, a2 , a3 need to be transformed before u obtained the estimates of the model". Can you please clarify here? Your program has immensely helped me out. I am learning and doing how all those stops exactly implement in stata. I appreciate it. Nilam On Thu, Mar 10, 2011 at 1:48 PM, Fernando Rios Avila <f.rios.a@gmail.com> wrote: > Hi Nilam > First of all, i should say you have to start by running your model without > any constrain to see if the data doesnt reject your constrains. > If your constrains are not rejected, you could just program the ML function > in stata. Im assuming u are familiar with the ML in stata, so i ll just > write the code: > > program define MLlinear > args lnf a1 a2 a3 sigma > tempvar y x1 x2 x3 error > tempvar b1 b2 b3 error > gen double `y' =$ML_y1 > gen double `x1'=$ML_y2 > gen double `x2'=$ML_y3 > gen double `x3'=$ML_y4 > > now to be sure your constrains are imposed > gen double `b1'=exp(`a1')/(1+exp(`a1')) > and for the rest just functions of the first one > gen double `b2'=exp(`a2')/(1+exp(`a2'))*(1-`b1') > gen double `b3'=exp(`a3')/(1+exp(`a3'))*(1-`b1'-`b2') > gen double `error'=`y'-`b1'*`x1'-`b2'*`x2'-`b3'*`x3' > replace `lnf'=log(normalden(`error',`sigma') > end > after this, and afterthe program is called > ml model lf MLlinear (a1:y x1 x2 x3=) (a2:) (a3:) (sigma:), maximize > just remember that the parameters a1, a2 , a3 need to be transformed before > u obtaine your estimates of the model. > Good luck > > On Thu, Mar 10, 2011 at 1:06 PM, Fernando Rios Avila > <ecofrax@langate.gsu.edu> wrote: >> >> >> ---------- Forwarded message ---------- >> From: Nilam Prasai <nilamprasai@gmail.com> >> To: statalist@hsphsun2.harvard.edu >> Date: Thu, 10 Mar 2011 13:04:29 -0500 >> Subject: st: Estimating linear regression model by maximum likelihood >> Dear Stata Users, >> I am trying to estimate a linera regression by maximum likelihood >> while imposing the constraints. >> I am trying to regress GDP/capita on three independent variables while >> imposing the constraint on parameter estimates. >> The constraint on my coefficeint estimate/parameter efficient will be >> that: non of the coefficient estimate should be negative and the sum >> of the coefficients for all the three variable should not be greater >> than 1 meaning that they should lie between zero to one while their >> sum (sum of three variables) should not be more than 1. >> >> Please help me out how can I do this in Stata. >> Nilam Prasai >> >> * >> * 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/ >> Content-Type: text/plain; charset=US-ASCII >> Content-Transfer-Encoding: quoted-printable >> Content-Disposition: inline >> >> Dear Stata Users, >> I am trying to estimate a linera regression by maximum likelihood >> while imposing the constraints. >> I am trying to regress GDP/capita on three independent variables while >> imposing the constraint on parameter estimates. >> The constraint on my coefficeint estimate/parameter efficient will be >> that: non of the coefficient estimate should be negative and the sum >> of the coefficients for all the three variable should not be greater >> than 1 meaning that they should lie between zero to one while their >> sum (sum of three variables) should not be more than 1. >> >> Please help me out how can I do this in Stata. >> Nilam Prasai >> >> * >> * 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/ >> >> > > -- Nilam Prasai * * 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/