Dear All,

I am working with non-linear estimation and for this reason I have included measurement errors.

The dataset is generated from a third-order (stoch_simul, order=3) approximation of the model.

When I run the mode_compute step (using CMAES, algorithm number 9 with 1 000 000 function evaluations and 100 000 iterations) , I obtain the attached mode_check plot.

I see the line for the mode is centered on the maximum of the log-likelihood BUT the standard deviations of the measurement errors touch their respective prior bounds.

Can I trust these values or should I interpret this as an identification issue?

How would you solve this issue?

Many thanks

DB

posterior.pdf (54.3 KB)

estimated_params_init;

alp, 0.4;

bet, 0.99;

tet, 0.357;

tau, 50;

delt, 0.02;

rho, 0.95;

stderr e_a, .035;

stderr y, .0175;

stderr l, .00312;

stderr i, .00465;

end;

estimated_params;

alp, uniform_pdf, 0.0001, 0.99;

bet, beta_pdf,0.97,0.025;

tet, uniform_pdf, 0.0001, .999;

tau, uniform_pdf, 0.0001, 100;

delt, uniform_pdf, 0.0001, 0.05;

rho, beta_pdf,0.95,0.04;

stderr e_a, inv_gamma_pdf,0.035,4;

stderr y, uniform_pdf, 0.00001, 0.1;

stderr l, uniform_pdf, 0.00001, 0.1;

stderr i, uniform_pdf, 0.00001, 0.1;

end;