Bayesian time estimation issue

Hello guys, I am trying to replicate a paper that does a bayesian estimation of a DSGE model with bubbles. The model is kinda big and it estimates 19 parameters using bayesian methods and it has 2,000,000 draws of the MH algorithm. I need the posterior mode and mean and the whole estimation process takes about 17 hours. I wonder if there is a way to save time in doing the estimation. I tried the following:

first I calculate the whole MH using the following command

it works fine and it gives the mean, the mode, and all the info I need

, however, since I did not get the results that I wanted I tried changing the drop and the jump scale values, but using the MH draws that I already have by using the following command,

and I got the similar results that shows in [Oo_.posterior_mean is missing)
when I use mh_replic=0 in combination with load_mh_file I just get the new posterior mod, but not the posterior mean.
is there a way around this?
Best Mario

Have you tried the recent snapshot? What exactly is the problem?

Based on my current understanding, the answer is no. You are loading a mode_file but don’t run an MCMC chain. What is changing the percent of draws dropped at the beginning and the dispersion of the proposal density supposed to do when you are not running a chain?
If you just want to drop more draws at the beginning, you could do that manually by loading the mh-files.

Sorry, maybe I was not clear (tends to happen). I need to do a Bayesian estimation of a DSGE model. I need to do 2,000,000 draws (that is what the paper that I am replicating says). I have estimated the model once, can I use those 2MM draws that I get for the first estimation to get another estimate if, for example, I see that the acceptation rate is not good?

As I tried to state before I use the mh_replic=0 because I already have all the draws that I need, but I still need to get the mean and mode.
I am sorry if I am not clear, I am new using dynare and doing bayesian estimation