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sim_trans_irf.m
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sim_trans_irf.m
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if ~exist('clear_flag', 'var'), clear_flag = 1; end
if usejava('desktop') && clear_flag
clear;
else
ver;
end
close all;
%--------------------------------------------------------------------------
% simulation setup
%--------------------------------------------------------------------------
% file with model
respath='./';
outpath='./Results/';
if ~exist('resfile_list','var')
% From where it will read the results.
resfile_list={'res_20200904_bench'};
end
for f=1:length(resfile_list)
resfile=resfile_list{f};
load([respath,resfile,'.mat']);
% Initial Economy Config
varlist={'simseries','statevec','indexmap','varnames'};
load(['sim_',resfile],varlist{:});
% set starting point
start_ini=3;
start_shock=[0,1,2]; %[0,3,4];
statevec=statevec(2:end);
% Take the values of the simulated variables which are related to the
% initial shock = 3, take the mean of all the variables, this will give
% me 107 values for each variable.
startvals=mean(simseries(statevec==start_ini,:));
N_vars=length(startvals);
% number of periods and burn-in
N_shock=length(start_shock);
N_runs=5000;
NT_sim=25;
NT_ini=0;
NT_sim=NT_sim+1;
% cluster endogenous states
envarind=4:5;
maxclust=10;
% Take only those values for which the exogenous state variable equals
% start_ini.
enstatemat=simseries(statevec==start_ini,envarind);
% Divide the data in 10 clusters
cindex=clusterdata(enstatemat,'criterion','distance','maxclust',maxclust,'linkage','weighted');
% Number of simulatiosn that are related to statevec==start_ini
sttot=size(enstatemat,1);
% Initializing matrix
startptmat=[];
for c=1:maxclust
%fraction of simulations in this cluster
cfrac=sum(cindex==c)/sttot;
% mean for the two state variables in this cluster
thismean=mean(enstatemat(cindex==c,:),1);
% print information
disp([num2str(c),': ',num2str(thismean)]);
thisc=repmat(thismean,floor(N_runs*cfrac),1);
% Store the values of the means
startptmat=[startptmat; thisc];
end
% To have vectors of the same size, just fill the last rows (until
% I have 5,000 simulations) with the overall mean.
if size(startptmat,1)<N_runs
thismean=mean(enstatemat);
startptmat=[startptmat; repmat(thismean,N_runs-size(startptmat,1),1)];
end
% report levels or growth rates for output variables
reportLevels=1;
% compute Euler equation error?
compEEErr=1;
% make graphs grayscale
grayscale=0;
% Compute term premium (slow!)
term_premium=0;
% output table file
outfile=['GR_',resfile];
% Re-storing the name of the variables.
varnames_store = varnames;
% Initialiing the vectors to store the results
simseries_median = cell(N_shock,1);
simseries_mean = cell(N_shock,1);
simseries_std = cell(N_shock,1);
simseries_diff_median = cell(N_shock,1);
simseries_diff_mean = cell(N_shock,1);
simseries_diff_std = cell(N_shock,1);
open_parpool;
for s=1:(N_shock)
disp(['Shock ',num2str(s),' of ',num2str(N_shock)]);
tens_simseries = zeros(NT_sim,N_vars,N_runs);
% Compute entry of random number matrix that sets first state
% deterministically to start_shock
% For the first shock this part is not relevant.
if start_shock(s)>0
transprob=cumsum(mobj.Exogenv.mtrans(start_ini,:));
shock_prob=transprob(start_shock(s));
if start_shock(s)>1
shock_prob_minus=transprob(start_shock(s)-1);
else
shock_prob_minus=0;
end
rvar_next=(shock_prob+shock_prob_minus)/2;
end
% Create shock matrix
rng(1);
shmatfull = lhsdesign(N_runs,NT_sim);
SDFmat=zeros(NT_sim,mobj.Exogenv.exnpt);
fprintf([repmat('.',1,100) '\n\n']);
parfor n=1:N_runs
%--------------------------------------------------------------------------
% start simulation
%--------------------------------------------------------------------------
%fprintf('Run %d - Start \n',n);
% simulate
shmat = shmatfull(n,:)';
%isinteger(shmat)
if start_shock(s)>0
shmat(1)=rvar_next;
end
startpt=struct;
startpt.whatM=startptmat(n,1);
startpt.eI=startptmat(n,2);
startpt=orderfields(startpt,mobj.En_names);
startpt_vec=model.DSGEModel.structToVec(startpt)';
startpt_vec=[start_ini,startpt_vec];
[simseries,varnames,~,~,~]=mobj.simulate(NT_sim,NT_ini,startpt_vec,compEEErr,shmat);
simseries_orig=simseries;
varnames_orig=varnames;
statevec = simseries(:,1);
%fprintf('Run %d - After simulation \n',n);
[simseries, varnames] = mobj.computeSimulationMoments(simseries,varnames);
nvars = length(varnames);
%fprintf('Run %d - After computation \n',n);
% disp(size(startvals))
% disp(size(simseries))
tens_simseries(:,:,n) = [startvals; simseries];
if mod(n,N_runs/100)==0
%disp([num2str(n),'/',num2str(N_runs),': ',num2str(round(1000*n/N_runs)/10),'% complete']);
fprintf('\b|\n');
end
end
fprintf('\n');
varnames = varnames_store;
nvars = length(varnames);
% make HashMap with mapping of names to indices
indexmap=java.util.HashMap;
for i=1:nvars
indexmap.put(varnames{i},i);
end
% varst=zeros(length(startpt_vec)-1,1);
% for i=1:length(startpt_vec)-1
% varst(i)=indexmap.get(mobj.En_names{i});
% end
%save(outfile,'tens_simseries','indexmap');
simseries_median{s} = median(tens_simseries,3);
simseries_mean{s} = mean(tens_simseries,3);
simseries_std{s} = std(tens_simseries,[],3);
if start_shock(s) > 0
% If actual shock, difference and save
tens_simseries_diff = tens_simseries - tens_simseries_0;
simseries_diff_median{s} = median(tens_simseries_diff,3);
simseries_diff_mean{s} = mean(tens_simseries_diff,3);
simseries_diff_std{s} = std(tens_simseries_diff,[],3);
else
% If no shock, store for later differencing
tens_simseries_0 = tens_simseries;
end
end
save(outfile,'simseries_mean','simseries_median','simseries_std', ...
'simseries_diff_mean', 'simseries_diff_median', 'simseries_diff_std', ...
'indexmap','NT_sim','N_shock');
end