Dear all, I would kindly ask two conceptual questions about estimation. i) I use the estimation command with mode_compute option to compute the mode with uniform priors for all parameters (large intervals). Then I use the mode results to simulate the model. From my perspective, this appears to be s…

See [image] The log marginal data density in mode_computer=6 ML/Bayesian estimation Both the Laplace approximation and the modified harmonic mean estimator are ways to compute the marginal data density. As computing the marginal data density involves solving a complicated …

Yes, that is equivalent to ML. No, frequentist comparisons based on the likelihood are only valid for nested models (likelihood ratio tests). For non-nested models you have to use Bayesian model comparison using the marginal data density. Also note that implicit prior truncation due to e.g. Blancha…

Thanks for your response, professor. I noticed that, accordingly to the manual, the output oo_.MarginalDensity.LaplaceApproximation Is produced after the estimation command, even with mh_replic = 0. It is enough to compare this statistic between the models?

Perfect, so we do not need metropolis for the laplace approximation algorithm. I’ll work with that. As always, many thanks for your availability Professor !

Estimation with mode_compute only and model comparison

Estimation ML/Bayesian estimation

jpfeifer 5 November 2024 13:25 4

See