[Gretl-users] The local linear trend model estimation (Kalman filter)

Dear Gretl Community, I'm trying to replicate the results from Commandeur & Koopman (2007) (chapter 3, section 3.2, pp. 23-26) but I'm getting no success :-( Please take a look at the code I'm running (the .inp file is attached): <hansl> set echo off set messages off clear open "UK-KSI.gdt" --frompkg=StrucTiSM series y = log(KSI) setinfo y --description="log UK drivers KSI against time (in months)" --graph-name="log UK drivers KSI against time (in months)" ##### 3.2. Stochastic level and slope ##### /* parameter initialization */ scalar var_irreg = 1 scalar var_level = 1 scalar var_slope = 1 /* state-space model setup */ matrix H = {1 ; 0} matrix F = {1, 1; 0, 1} matrix Q = {var_level, 0; 0, var_slope} bundle kalman = ksetup(y, H, F, Q) kalman.obsvar = var_irreg kalman.diffuse = 1 /* maximum likelihood estimation */ "stochastic KSI" <- mle ll = ERR ? NA : kalman.llt kalman.obsvar = var_irreg kalman.statevar[1, 1] = var_level kalman.statevar[2, 2] = var_slope ERR = kfilter(&kalman) params var_irreg var_level var_slope end mle --lbfgs </hansl> The results from Commandeur & Koopman (2017) are: observation variance estimate (irregular): 0.002118 state variance estimate (level): 0.012128 state variance estimate (slope): 0.000000 Any help will be very appreciated. Best regards, Henrique Andrade