On Thu, 17 May 2012, Nrz Mrva wrote: Gretl's "fcast" command is not available for multinomial logit at present, partly because it's not very clear what a forecast should look like for that sort of model. However, it's not very difficult to write a script that will produce the estimated probability for each outcome, given the estimated parameters and some data. You can then pick the outcome with the highest probability as the "forecast" if you like. Here's an example script ("hansl" is gretl's scripting language): <hansl> function matrix mlogit_probs (matrix b, matrix X, matrix y) scalar m = rows(y) scalar nb = rows(b) / (m-1) scalar T = rows(X) matrix probs = zeros(T, m) matrix num = ones(1, m) loop t=1..T --quiet scalar den = 1 k = 1 loop j=2..m --quiet matrix bj = b[k:k+nb-1] num[j] = exp(X[t,]*bj) den += num[j] k += nb endloop probs[t,] = num / den endloop return probs end function open keane.gdt smpl year==87 --restrict list Xlist = const educ exper expersq black matrix yvals = values(status) # estimate, reserving the last 20 observations smpl ; -20 logit status Xlist --multinomial matrix theta = $coeff # now forecast for the last 20 obs smpl ; +20 sstart = $nobs - 19 smpl sstart ; matrix X = {Xlist} matrix probs = mlogit_probs(theta, X, yvals) # show the probabilities per outcome print probs # show the most probable outcome matrix fc = imaxr(probs) print fc </hansl> Allin Cottrell