Hi,

I’m trying to estimate a simple RBC model with three parameters. I’m using mode_compute=6 since other optimizers find a non-positive Hessian. However, the results are in the boundaries of the priors and not very close to the true values (the data used is synthetic data generated with alpha=0.3, betap=0.2 and rho=0.9).

I use these priors:

alpha, 0.3, 0.2, 0.5, normal_pdf, 0.3, 0.025;

betap, 0.2, gamma_pdf, 0.25, 0.1;

rho, 0.9, beta_pdf, 0.5, 0.2;

With first order I get these results:

parameters

prior mean post. mean 90% HPD interval prior pstdev

alpha 0.300 0.2005 0.2000 0.2017 norm 0.0250

betap 0.250 1.3170 1.3187 1.3294 gamm 0.1000

rho 0.500 0.9997 0.9997 0.9998 beta 0.2000

And with second order

parameters

prior mean post. mean 90% HPD interval prior pstdev

alpha 0.300 0.3000 0.3000 0.3000 norm 0.0250

betap 0.250 0.2000 0.2000 0.2000 gamm 0.1000

rho 0.500 0.9000 0.9000 0.9000 beta 0.2000

Thank you for the help

fake_data_1.csv (5.0 KB)

fake_data_2.csv (8.3 KB)

rbc.mod (2.2 KB)