New samplers and mode compute methods

Dear All,
I was reading slides on non-linear estimation from the 2019 Dynare Summer-school and I saw you were already testing a version of Herbst and Shorfheide SMC sampler based on tempered likelihood and a Dynamic Striated MH sampler.
Are they available in Dynare 4.6.3?
In positive case, what are the settings to call them?

Also, I saw in the 4.6 manual, there is a new non-gradient based algorithm for computing the mode using the particle-swarm Matlab optimization algorithms (mode_compute = 12).
When is this method more advised than others?

Many thanks in advance for your availability!

  1. From what I can see, the codes are there, e.g. https://git.dynare.org/Dynare/particles/-/commit/22be3797a87b2f9ce5ae75556ab18774088d66ee
    However, there is no documentation and no examples yet. So getting them to work may be challenging. Maybe @frederic.karame can help.
  2. The particle swarm is a global optimizer. So you can give it a try in challenging situations. But my experience is that it tends to be really slow.

Herbst and Shorfheide SMC sampler and the Dynamic Striated MH sampler have only been tested on the toy model I use at the summer school, for a few filters. But we also need to develop an interface to use them and to add the documentation…

@frederic.karame Could you provide the toy model files?

@jpfeifer The toy model is dsge_base2.mod.

I meant how to invoke the two samplers. https://git.dynare.org/Dynare/dynare/-/blob/master/tests/particle/dsge_base2.mod does not seem to have those cases included.

To test, I put a call in dynare_estimation1.m

Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
or
DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)

a priori everything is defined in Global_initialization.m with comprehensive notations (DSMH_ for DSMH) and HB_ for Herbst &Schorfheide