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RE: st: Nonlinear model using maximum likelihood


From   jverkuilen <[email protected]>
To   <[email protected]>
Subject   RE: st: Nonlinear model using maximum likelihood
Date   Wed, 4 Mar 2009 08:43:43 -0500

Not sure if this helps, but what error distribution are you using? ML is going to need a likelihood which requires you to pick an error model, but you only have a mean structure. 

-----Original Message-----
From: "Magnus Soderberg" <[email protected]>
To: "[email protected]" <[email protected]>
Sent: 3/3/2009 5:39 PM
Subject: st: Nonlinear model using maximum likelihood

Dear All,

I want to estimate the following nonlinear function:

Y = b0 + b1*exp(b2*x1)*b4*x2*x3 + Xb 

Where b0 to b3 are parameters (b vector of parameters), x1 to x3 variables (X a vector of variables). 

I could estimate this by using the Stata command nl, but I want to use ML. 

There is a similar problem and a solution posted at http://www.stata.com/support/faqs/stat/nl_ml.html which looks like

program mlnexpgr
	version 10
	args lnf b1x b0 sigma
	tempvar res
	quietly gen double `res' = $ML_y1 - `b0'*(1-exp(-`b1x'))
	quietly replace `lnf' = -0.5*ln(2*_pi)-ln(`sigma')-0.5*`res'^2/`sigma'^2
end

ml model lf mlnexpgr (b1: rep78 = headroom, nocons) (b0:) (sigma:)
  
ml max


I guess my problem is the specification of the "ml model"-line but despite numerous attempts I can't get a reasonable output.

Does anyone know how to do this?


All the best,
Magnus


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