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?
posterior.pdf (54.3 KB)
stderr e_a, .035;
stderr y, .0175;
stderr l, .00312;
stderr i, .00465;
alp, uniform_pdf, 0.0001, 0.99;
tet, uniform_pdf, 0.0001, .999;
tau, uniform_pdf, 0.0001, 100;
delt, uniform_pdf, 0.0001, 0.05;
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;