Hi everyone,
First of all, a big ‘thank you’ to everyone involved in the dynarejulia project, everything I’ve tried so far works very well and the speed gains over Matlab are very impressive.
I had a few questions when using it for simple estimation and forecasting tests.

Is it possible to incorporate the parameters in a seperate file, as it was possible in Matlab to do something like: @#include “parameter_file.m”.

Is it possible to have a seperate file to compute the steady state, as it was possible in Matlab to have a file like “model_steadystate” starting with:
function [ys,params,check] = model_steadystate(ys,exo,M_,options_)
%
% Inputs:
%  ys [vector] vector of initial values for the steady state of
% the endogenous variables
%  exo [vector] vector of values for the exogenous variables
%  M_ [structure] Dynare model structure
%  options [structure] Dynare options structure
etc

When running the estimation command, I noticed that there are no mode_check plots, and the ‘bayesian_irf’ option seems not there yet. Is the plan to add this later? Or is it already there and I just overlooked it, failed to activate the option properly?

When running the forecast! function, I managed to get it running for the calibsmoother, but this seems to always produce a forecast based on the calibrated paramaters, not based on the estimated parameters. How could one produce forecasts based on the estimated parameters?

Is there a way to get confidence bands for the forecasts? Or is this not ready yet? [I guess one alternative would be to just sample from the chain and run the calibsmoother based forecast, using a sampled parameter vector each time]
Thank you so much!
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PS: Here is the example code I have been using (which is based on what people wrote in the forum in recent weeks).
var Y C K L A R W I OBS_C;
varexo eps_A;
parameters alph betta delt gam pssi rhoA sigmA;
alph = 0.35; betta = 0.99; delt = 0.025; gam = 1; pssi = 1.6;
rhoA = 0.9226; sigmA=0.0440;
model;
#UC = gam*C^(1);
#UCp = gam*C(+1)^(1);
#UL = pssi*(1L)^(1);
UC = betta*UCp*(1delt+R);
W = UL/UC;
K = (1delt)*K(1)+I;
Y = I+C;
Y = A*K(1)^alph*L^(1alph);
W = (1alph)*Y/L;
R = alph*Y/K(1);
log(A) = rhoA*log(A(1))+eps_A;
OBS_C=log(C)log(C(1));
end;
steady_state_model;
A = 1;
R = 1/betta+delt1;
K_L = ((alph*A)/R)^(1/(1alph));
W = (1alph)*A*K_L^alph;
I_L = delt*K_L;
Y_L = A*K_L^alph;
C_L = Y_LI_L;
L = gam/pssi*C_L^(1)*W/(1+gam/pssi*C_L^(1)*W);
C = C_L*L;
I = I_L*L;
K = K_L*L;
Y = Y_L*L;
OBS_C=CC;
end;
shocks;
var eps_A; stderr sigmA;
end;
steady;
check;
stoch_simul(order=1);
estimated_params;
//PARAMETER , INITIAL VALUE , PRIOR TYPE , PRIOR MEAN , PRIOR STDERR
rhoA , 0.9226 , beta_pdf , 0.9 , 0.1 ;
stderr eps_A , 0.0440 , inv_gamma_pdf , 0.1 , 0.1 ;
end;
varobs OBS_C;
estimation(order=1,
datafile='dataset_mj.csv',
mh_nblocks=4,
mh_replic=10000,
mh_jscale=20.);
forecasting!(periods=10, datafile="dataset_mj.csv", forecast_mode=calibsmoother);
%forecasting!(periods=10, datafile="dataset_mj.csv", forecast_mode=histval); % does not work
% mode_check, % does not work
% bayesian_irf % does not work
dataset_mj.csv (1.6 KB)