Hello,

I am trying to estimate (Bayesian) a very simple filter model. My model is non-stationary, so I am using the diffuse filter.

When I try and run the model, dynare gets stuck at what appears to be a minimisation routine (“Initial value of the log posterior (or likelihood): -Inf”).

I believe this is dynare trying to find a plausible steady state for the model. I wasn’t clear why it needed to do this given the model is already linearized. I am also able to run the exact same model in IRIS and perform a maximum likelihood estimation with no problem.

I am probably missing something obvious but would appreciate any pointers. Thanks! (can post data and code):

var LGDP LGDP_BAR Y G PIE GROWTH InfE UNR UNR_BAR UNR_GAP G_UNR_BAR LGDP_ PIE_ UNR_ InfE_ GROWTH_;

varexo RES_LGDP_BAR RES_G RES_Y RES_PIE RES_InfE RES_UNR_GAP RES_UNR_BAR RES_G_UNR_BAR;

parameters beta lambda lambda2 phi growth_ss tau1 tau2 tau3;

lambda= 0.6896;

lambda2 = 0.2;

beta = 0.1032;

phi = 0.9882;

tau1 = 0.1946;

tau2 = 0.5522;

tau3 = 0.9;

model(linear);

LGDP = LGDP_BAR + Y;

```
// Stochastic process for potential output level
LGDP_BAR = LGDP_BAR(-1) + G + RES_LGDP_BAR;
// Stochastic process for growth rate of potential
G = G(-1) + RES_G;
// Stochastic process for output gap
Y = phi*Y(-1) + RES_Y;
// Philips curve
PIE = lambda*InfE+(lambda2)*PIE(-1) + beta*Y + RES_PIE;
// Inf E
InfE = InfE(-1)+RES_InfE;
// Growth definition
GROWTH = LGDP-LGDP(-1);
// NAIRU definition
UNR_BAR = UNR + UNR_GAP;
// Dynamic Okun's law
UNR_GAP = tau2*UNR_GAP(-1) + tau1*Y + RES_UNR_GAP;
// Stochastic process for NAIRU
UNR_BAR = UNR_BAR(-1) + G_UNR_BAR + RES_UNR_BAR;
// Stochastic process for the change in NAIRU
G_UNR_BAR = (tau3)*G_UNR_BAR(-1) + RES_G_UNR_BAR;
// observation equations
LGDP_ = LGDP;
PIE_ = PIE;
InfE_ = InfE;
GROWTH_ = GROWTH;
UNR_ = UNR;
```

end;

shocks;

var RES_LGDP_BAR; stderr 0.0328;

var RES_G; stderr 0.0324;

var RES_Y; stderr 0.5772;

var RES_PIE; stderr 0.4104;

var RES_InfE; stderr 0.0587;

var RES_UNR_GAP; stderr 0.0695;

var RES_UNR_BAR; stderr 0.0251;

var RES_G_UNR_BAR; stderr 0.0322;

end;

initval;

LGDP = 0;

LGDP_BAR = 0;

Y = 0;

G = 0;

PIE = 0;

GROWTH = 0;

InfE = 0;

UNR = 0;

UNR_BAR = 0;

UNR_GAP = 0;

G_UNR_BAR = 0;

LGDP_ = 0;

PIE_ = 0;

InfE_= 0;

UNR_ = 0;

end;

//steady;

//check;

//check;

estimated_params;

lambda, normal_pdf, 0.5, 0.4;

lambda2, normal_pdf, 0.5, 0.4;

beta, normal_pdf, 0.1, 0.2;

phi, normal_pdf, 0.6, .2;

tau1, normal_pdf, 0.2, 0.2;

tau2, normal_pdf, 0.3, 0.2;

tau3, normal_pdf, 0.5, inf;

stderr RES_LGDP_BAR, inv_gamma_pdf, 0.1, inf;

stderr RES_G, inv_gamma_pdf, 0.1, inf;

stderr RES_Y, inv_gamma_pdf, 1, inf;

stderr RES_InfE, inv_gamma_pdf, 1, inf;

stderr RES_PIE, inv_gamma_pdf, 1, inf;

stderr RES_UNR_GAP, inv_gamma_pdf, 1, inf;

stderr RES_UNR_BAR, inv_gamma_pdf, 0.1, inf;

stderr RES_G_UNR_BAR, inv_gamma_pdf, 0.1, inf;

end;

varobs LGDP_ PIE_ UNR_ InfE_ GROWTH_;

estimation(order=1, datafile=import_dyn, nobs=74, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8, diffuse_filter);