Thank you for this comment. I am aware of this issue. However, I don’t quite see why parameter dependence is an issue here. In fact the model has a steady state at biglamda=1.02. No obvious problem is detected in model diagnostics.

The problem only occurs at the estimation level. I am setting the prior in such a way that biglamda is plausible. This is based on Japanese data and biglanda=1.02 may be too high. Thus I don’t see what is really the issue here.

STEADY-STATE RESULTS:

m 0.951557

iL 0.0509091

k 20.635

h 1.75179

y 4.22659

c 2.31622

i 0.910368

d 1.04818

mR 0.994868

mT 0.760232

mRprop 0.566844

mm 1.03037

S 15.1376

pii 0.02

Q 1

realW 1.15811

Adj 0

Adjprime 0

P_Pw 1.33333

A 1

div 0.10784

Debt 9.82259

ytd 1.05091

Rytd 1.0303

hpr 1.05091

rhpr 1.0303

rk 0.030303

bg 0.648886

G 1

T 0.991378

lamda 0.4

Zx 1

ip 0.001

ytm2 0.0296556

Sr 22

tp 0.0198026

nytm 0.0496556

mR_d 0.949142

log_y 1.44139

log_c 0.839936

log_i -0.0939066

log_G 0

log_mR -0.0051453

log_mT -0.274131

log_d 0.0470518

log_h 0.560637

log_Q 0

log_S 2.71718

log_k 3.02699

mbgrate 1.02

y_obs 0

i_obs 0

G_obs 0

c_obs 0

mR_obs 0

mT_obs 0

d_obs 0

h_obs 0

k_obs 0

Q_obs 0

S_obs 0

MODEL_DIAGNOSTICS: No obvious problems with this mod-file were detected.

I will be grateful if you please elaborate a bit on this.