likelihood vs marginal likelihood in model comparison
Posted: Tue Mar 01, 2016 5:41 pm
Dear all,
I'm a bit confused about model comparison. I'm estimating with Bayesian methods. As I understand, what Dynare calls `data density' is what others call `marginal data density' or `marginal likelihood'. As such, it discriminates with respect to priors: data density is lower if the posterior is further away from the prior.
However, I'm not interested in discriminating with respect to priors. I'm interested in which model is closer to the data regardless of my priors. So, my interested object is (correct me if I'm wrong) likelihood at the posterior mode (or mean) but which is not weighted by prior densities.
In this respect,
1) can I get the likelihood from Dynare?
2) what are the two objects in the log-file: `initial/final value of the log posterior (or likelihood)', and why is the final value with the opposite sign (I think they should have the same sign when starting at optimal initial values).
I'm a bit confused about model comparison. I'm estimating with Bayesian methods. As I understand, what Dynare calls `data density' is what others call `marginal data density' or `marginal likelihood'. As such, it discriminates with respect to priors: data density is lower if the posterior is further away from the prior.
However, I'm not interested in discriminating with respect to priors. I'm interested in which model is closer to the data regardless of my priors. So, my interested object is (correct me if I'm wrong) likelihood at the posterior mode (or mean) but which is not weighted by prior densities.
In this respect,
1) can I get the likelihood from Dynare?
2) what are the two objects in the log-file: `initial/final value of the log posterior (or likelihood)', and why is the final value with the opposite sign (I think they should have the same sign when starting at optimal initial values).