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Re: st: Nonlinear regression and constraints
On 29. jun. 2005, at 08.49, Daniel Schneider wrote:
I think the easiest way to do this is to define a function g:R-> (0,1),
Without going to much into detail: my parameters are percentages. They
can only range from 0 to 1. There may be a better solution (i.e. a
solution that better fits the data) beyond 1, BUT, as I said, by
definition they cannot be above 1 (or below 0). So the best solution
that is possible has to be between 0 and 1.
and then where you want to estimate a parameter \theta \in (0,1),
you instead make the transformation of variables
\theat = exp(x)/(1+exp(x)).
So stata optimises to find an x \in R, but you know that the estimate
of your parameter is \theta^*=exp(x^*)/(1+exp(x^*)). You can use
asymptotic theory to find the standard errors of this transformation.
Or you can estimate first with this method and then reestimate with
starting points close to the estimates and hope that it converges to
the same estimates instead of going off out of the allowed parameter
I'm not sure it is a good idea to try to adopt to the possibility
that \theta=0 or \theta=1, at least all standard asymptotic
distribution theory would break down at the limits of the parameter
Erik Ø. Sørensen, dept of Econ., Norwegian School of Economics
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