On Wed, 2011-10-26 at 21:28 +0200, Riccardo (Jack) Lucchetti wrote: > On Wed, 26 Oct 2011, Giuseppe Vittucci wrote: > > > The following code (adapted from the manual): > > > > mle logl = check ? - ln(pstr_cssr(y,X,q,gamma,c,m,Z) : NA > > scalar check = (gamma > zeros(r,1)) && (c >= c_min) && (c <=c_max) > > params gamma c > > end mle > > > > simply checks that the unconstrained maximum is in the parameter space > > and returns an error if it is not so. > > But is there a way to find such a constrained maximum in gretl? > > > > If there is not, what is the best way to circumvent the problem? > > There isn't. The idea is to check whether the parameters are admissible > when the loglik is computed, just like you're doing. > > A couple of tips: > > 1) I suppose gamma is a vector; you can achieve the same result as what > I think you're tryng to do by writing (gamma > 0) (cool, huh?)
Cool ;-) > > 2) if you have constrained parameters, you're probably much better off if > you use some 1-to-1 transformation to some unbounded parameter. In your > example, assuming you really mean to use <= and >= rather than strict > inequalities, you may use > > mle logl = ln(pstr_cssr(y,X,q,gamma,c,m,Z) > matrix gamma = exp(lg) > scalar c = c_min + 0.5*(sin(ac) + 1)*c_max > params lg ac > end mle > > and then retrieve the $vcv for your original parametrisation by the delta > method. Otherwise, if what you mean is really c_min < c < cmax (more > customary in ml problems), then a nicer alternative is > > scalar c = c_min + 0.5*(tanh(ac) + 1)*c_max > > or perhaps > > scalar c = c_min + cnorm(ac)*c_max > > HTH, Thanks. Very nice tricks. PS: Also c, c_min and c_max were vectors but it doesn't matter. ;-) > > Riccardo (Jack) Lucchetti > Dipartimento di Economia > Università Politecnica delle Marche > > r.lucchetti(a)univpm.it > http://www.econ.univpm.it/lucchetti > _______________________________________________ Gretl-users mailing list > Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users
