Re: [Gretl-users] Non-linear least squares

On Wed, 30 Apr 2014, Riccardo (Jack) Lucchetti wrote: I wrote a few functions to compute a regression in which coefficients are constrained to be strictly positive and sum to one. This was quite fun, but not vey much tested. It would be VERY nice if someone did some more work on this and turned it into a function package (hint, hint!) <hansl> set echo off set messages off function matrix shares(matrix b) matrix e = exp(b | 0) scalar den = sumc(e) return e ./ den end function function matrix dshares(matrix b) matrix p = shares(b) scalar k = rows(b) matrix ret = -p .* p[1:k]' ret[diag] += p[1:k] return ret end function function bundle OLS_shares(series y, list X) scalar k = nelem(X) # initalisation ols y X --quiet matrix b = ($coeff .> 0.01) ? $coeff : 0.01 matrix b = ln(b[1:k-1]) - ln(b[k]) nls y = lincomb(X, p) p = shares(b) J = dshares(b) deriv b = {X} * J end nls --quiet bundle mdl mdl["Xnames"] = varname(X) mdl["coeff"] = p mdl["vcv"] = qform(J, $vcv) return mdl end function function void printout(bundle model) string parnames = model.Xnames matrix cs = model.coeff ~ sqrt(diag(model.vcv)) modprint cs parnames end function # ---------------------------------------------------- nulldata 1000 x1 = normal() x2 = normal() x3 = normal() y = 0.3*x1 + 0.01*x2 + 0.69*x3 + normal() list X = x1 x2 x3 b = OLS_shares(y, X) printout(b) </hansl> ------------------------------------------------------- Riccardo (Jack) Lucchetti Dipartimento di Scienze Economiche e Sociali (DiSES) Università Politecnica delle Marche (formerly known as Università di Ancona) r.lucchetti(a)univpm.it http://www2.econ.univpm.it/servizi/hpp/lucchetti -------------------------------------------------------