Basically, in this paper, it is implemented and calibrated a State Space model using a Kalman Filter, where exogenous variables appear in both Measurement Equation and State Transition Equation. Hence, I'd like to know if it is possible to implement and calibrate such kind of model in GRETL. If yes, how would I do that?

Dear Sven, Thanks a lot for your attention. What you've commented makes a lot of sense to me. So actually, I would have a vector of Time Varying Coefficients (that would be my exogenous variables), and a vector of unitary constants, being added to the State? In any case, I'd be very happy if Jack could help me out.

Dear Sven, Thanks a lot for your attention. What you've commented makes a lot of sense to me. So actually, I would have a vector of Time Varying Coefficients (that would be my exogenous variables), and a vector of unitary constants, being added to the State?

Em 21/08/2015, às 18:25, Riccardo (Jack) Lucchetti <r.lucchetti(a)univpm.it&gt; escreveu: > On Fri, 21 Aug 2015, Sven Schreiber wrote: > >> Am 21.08.2015 um 16:37 schrieb Fernando Fernandes Neto: >> Dear Sven, >> >> Thanks a lot for your attention. >> What you've commented makes a lot of sense to me. So actually, I would >> have a vector of Time Varying Coefficients (that would be my exogenous >> variables), and a vector of unitary constants, being added to the State? > > Forget my remark about the constant, that was probably off-track. What I > have in mind is more or less the following. Yeah, Sven's approach is very sensible and arguably the most natural. What follows is an example script in which I simulate and estimate a model like this: y_t = alpha_t + epsilon_t alpha_t = x_t + 0.5*alpha_{t-1} + u_t which is more or less like the example Sven suggested, with y_t and x_t are observable series, epsilon_t and u_t are wn processes, and alpha_t is the latent state variable. I guess you can adapt it to your needs relatively easily. <hansl> set echo off set messages off /* generate some data */ nulldata 256 set seed 21082015 # make things replicable setobs 1 1 --special-time-series series exo = 5 exo = 1 + 0.8 * exo(-1) + normal() # this could be anything, really series alpha = 10 series u = 0.5*normal() series e = 0.2*normal() alpha = exo + 0.5*alpha(-1) + u series y = alpha + e /* set up the state space model */ function void TV_statemat(matrix *F, matrix x) F[1,2] = x[$kalman_t] end function matrix mexo = {exo} scalar se = 0.5 scalar su = 0.7 matrix H = {1;0} matrix F = {1, 1; 0, 1} matrix Omega = {1,0;0,0} matrix a0 = {0; 1} matrix P0 = {1.0e12, 0; 0, 0} kalman obsy y obsymat H obsvar su statemat F ; TV_statemat(&F, mexo) statevar Omega inistate a0 inivar P0 end kalman /* now estimate */ matrix param = {0.5, 1, 1} matrix ahat series LL = 0 mle LL = err ? NA: $kalman_lnl su = param[2]^2 Omega[1,1] = param[3]^2 F[1,1] = param[1] err = kfilter() params param end mle -vh ahat = ksmooth() series latent_est = ahat[,1] print alpha latent_est -o </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 ------------------------------------------------------- _______________________________________________ Gretl-users mailing list Gretl-users(a)lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-users