Re: [Gretl-users] AR-GARCH-t MLE

On Tue, 20 Mar 2012, Daniel Bencik wrote: [...] It's difficult to see what goes wrong without looking at the data. On my machine, the script below (in which I use your code almost unmodified, except for starting values and the usage of the lngamma() function instead of log(gammafun())) runs just fine. At the end of the script, you also get an example of how to estimate the same model using gig. <hansl> open djclose.gdt -q y = 100*ldiff(djclose) # allRng[t] = c + a1*allRng[t-1] + e[t] scalar c = 0.05 scalar a1 = 0.025 # e[t] = h[t] * Student[t] # GARCH - h[t] = cg + ga*h[t-1] + arc*e^2[t-1] scalar cg = 0.025 scalar ga = 0.95 scalar arc = 0.04 scalar dof = 6 mle ll = lngamma((dof + 1)/2) - lngamma(dof/2) - 0.5*log(dof-2) - \ log(h) - 0.5*(dof + 1)*log(1 + (dof - 2)^(-1)*h^(-2)*e^2) series e = 0 series e = y - c - a1*y(-1) #AR1 process series h = var(y) series h = cg + ga*h(-1) + arc*(e(-1))^2 # GARCH(1,1) params c a1 cg ga arc dof end mle /* ------ through gig ---------------------------------- */ include gig.gfn moo = gig_setup(y,1,const,null,1) gig_set_dist(&moo,1) gig_estimate(&moo) </hansl> Riccardo (Jack) Lucchetti Dipartimento di Economia Università Politecnica delle Marche (formerly known as Università di Ancona) r.lucchetti(a)univpm.it http://www2.econ.univpm.it/servizi/hpp/lucchetti