clear all; 
close all;

sdate = qq(2016,1);
edate = sdate + 15;
rng   = sdate:edate;
frng  = sdate-1:edate;

%% take the model
 
%  read model file 
m = model('nk.model','linear=',true);

% solve the model
m = solve(m);
m = sstate(m);
get(m,'sstate') 

%% simulate

rep = report.new('nk_model_behaviour');

db = zerodb(m,sdate);   % create zeros for all model variables,  
db.eps_y(sdate) = 1;
% db.eps_pie(sdate) = 1;
% db.eps_i(sdate) = 1;

sdb = simulate(m,db,rng,'deviation', true, 'anticipate',false);
s   = dbextend(db,sdb);

% plot IRFs
f1=figure;  
plot(frng,[s.y s.pie s.i],'linewidth',2,'marker','o');
legend('y','\pi','i');
ylabel('% odchylka od ss');
title('Poptavkovy sok');
grid on;

rep.userfigure('Poptavkovy sok',f1);

% dbplot(s,{'y','pie','i'});


%% simulate: nakladovy sok

db = zerodb(m,sdate);   % create zeros for all model variables,  
% db.eps_y(sdate) = 1;
db.eps_pie(sdate) = 1;
% db.eps_i(sdate) = 1;

sdb = simulate(m,db,rng,'deviation', true, 'anticipate',false);
s   = dbextend(db,sdb);

% plot IRFs
f2=figure;  
plot(frng,[s.y s.pie s.i],'linewidth',2,'marker','o');
legend('y','\pi','i')
title('Nakladovy sok');
grid on;

rep.userfigure('Nakladovy sok',f2);

%% simulate: monetarni sok

db = zerodb(m,sdate);   % create zeros for all model variables,  
% db.eps_y(sdate) = 1;
% db.eps_pie(sdate) = 1;
db.eps_i(sdate) = 1;

sdb = simulate(m,db,rng,'deviation', true, 'anticipate',false);
s   = dbextend(db,sdb);

% plot IRFs
f3=figure;  
plot(frng,[s.y s.pie s.i],'linewidth',2,'marker','o');
legend('y','\pi','i')
title('Monetarni sok');
grid on;

rep.userfigure('Monetarni sok',f3);

%% ocekavany vs. neocekavany

db = zerodb(m,sdate);   % create zeros for all model variables,  
db.eps_y(sdate+5) = 1;
% db.eps_pie(sdate+5) = 1;
% db.eps_i(sdate+5) = 1;

sdb = simulate(m,db,rng,'deviation', true, 'anticipate',true);
s   = dbextend(db,sdb);

% plot IRFs
f4=figure;  
plot(frng,[s.y s.pie s.i],'linewidth',2,'marker','o');
legend('y','\pi','i');
title('Poptavkovy sok');
grid on;

rep.userfigure('Ocekavany sok',f4);

%% vice vzadhledici PC

% vicenasobna parametrizace
m(2) = m(1);
m(2).b1 = 0.8; %vzadhledici pc
% m(2).c2 = 0;   %striktni inflacni cilovani 
% m(2).c1 = 2;   %vetsi vaha inflaci 

m = solve(m);

db = zerodb(m,sdate);   % create zeros for all model variables,  
db.eps_y(sdate) = 1;
% db.eps_pie(sdate) = 1;
% db.eps_i(sdate+5) = 1;

sdb = simulate(m,db,sdate:edate+10,'deviation', true, 'anticipate',true);
s   = dbextend(db,sdb);

% plot IRFs
f5=figure;  
subplot(221)
plot(s.y,'linewidth',2,'marker','o');
title('vystup');
grid;

subplot(222)
plot(s.pie,'linewidth',2,'marker','o');
title('inflace');
grid;

subplot(223)
plot(s.i,'linewidth',2,'marker','o');
title('urokova mira');
legend('benchmark','BWL PC')
grid;

rep.userfigure('Vzadhledici PC',f5);

%rep.publish('nk_obrazky4prednes.pdf','display',false);

%% model with smoothing of int. rate

% puvodni model
m = model('nk.model','linear=',true);
m = solve(m);
m = sstate(m);

db = zerodb(m,sdate);   % create zeros for all model variables,  
db.eps_y(sdate) = 1;
sdb = simulate(m,db,rng,'deviation', true, 'anticipate',false);
s   = dbextend(db,sdb);

% rozsireny model
n = model('nk2.model','linear=',true);
n = solve(n);
n = sstate(n);

sdbn = simulate(n,db,rng,'deviation', true, 'anticipate',false);
sn   = dbextend(db,sdbn);

f6=figure;  
subplot(221)
plot([s.y sn.y],'linewidth',2,'marker','o');
title('vystup');
grid;
subplot(222)
plot([s.pie sn.pie],'linewidth',2,'marker','o');
title('inflace');
grid;
subplot(223)
plot([s.i, sn.i],'linewidth',2,'marker','o');
title('urokova mira');
legend('benchmark','model with IR smoothing')
grid;