isolated functions deleted
git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1590 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
assia
parent
9692abe948
commit
38ecaa2f20
|
|
@ -1,8 +0,0 @@
|
|||
function CreateBenchmark
|
||||
|
||||
% stephane.adjemian@cepremap.cnrs.fr [12-06-2004]
|
||||
|
||||
global oo_
|
||||
|
||||
eval([M_.fname '_oo_ = oo_;'])
|
||||
eval(['save ' M_.fname '_benchmark_oo ' M_.fname '_oo_'])
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
function InitSeed
|
||||
|
||||
% stephane.adjemian@cepremap.cnrs.fr [12-02-2004]
|
||||
|
||||
normalseed = [ 220271 ;
|
||||
220378];
|
||||
|
||||
uniformseed = [ 0.6776 ; ...
|
||||
0.8239 ; ...
|
||||
0.6219 ; ...
|
||||
0.8169 ; ...
|
||||
0.2796 ; ...
|
||||
0.4337 ; ...
|
||||
0.6205 ; ...
|
||||
0.8994 ; ...
|
||||
0.9718 ; ...
|
||||
0.7993 ; ...
|
||||
0.4444 ; ...
|
||||
0.8896 ; ...
|
||||
0.9231 ; ...
|
||||
0.5838 ; ...
|
||||
0.1788 ; ...
|
||||
0.6556 ; ...
|
||||
0.7464 ; ...
|
||||
0.7545 ; ...
|
||||
0.2400 ; ...
|
||||
0.4431 ; ...
|
||||
0.3453 ; ...
|
||||
0.6687 ; ...
|
||||
0.6287 ; ...
|
||||
0.8371 ; ...
|
||||
0.8041 ; ...
|
||||
0.6802 ; ...
|
||||
0.5864 ; ...
|
||||
0.7713 ; ...
|
||||
0.8282 ; ...
|
||||
0.5748 ; ...
|
||||
0.0999 ; ...
|
||||
0.6360 ; ...
|
||||
0 ; ...
|
||||
0.0000 ; ...
|
||||
0.0000];
|
||||
|
||||
randn('state',normalseed);
|
||||
rand('state',uniformseed);
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
function check_mh(fname)
|
||||
eval(['load ' fname]);
|
||||
nb = size(x3,3);
|
||||
np = size(x2,2);
|
||||
nr = size(x2,1);
|
||||
|
||||
j1 = ceil(0.5*nr);
|
||||
x = [j1:100:nr];
|
||||
z = [];
|
||||
for i=1:np
|
||||
y1 = zeros(size(x),nb);
|
||||
for k=1:nb
|
||||
for j=1:length(x)
|
||||
y1(j,k) = mean(x2(1:x(j),i,k));
|
||||
end
|
||||
end
|
||||
my_subplot(i,np,4,5,'MH convergence');
|
||||
plot([y1])
|
||||
xmin = min(min(x2(:,i,:)));
|
||||
xmax = max(max(x2(:,i,:)));
|
||||
z = [z; [i sum(sum(x3(:,i,:) < xmin))/nr sum(sum(x3(:,i) > xmax))/nr]];
|
||||
end
|
||||
disp(z)
|
||||
|
||||
|
|
@ -1,171 +0,0 @@
|
|||
function compdist(xparam1, x2, pltopt, figurename)
|
||||
global bayestopt_ estim_params_ M_ options_
|
||||
|
||||
% NOTE: If pltopt ~= 'All' compdist.m just draws prior densities.
|
||||
|
||||
%% Set density estimation parameters:
|
||||
number_of_grid_points = 2^9; % 2^9 = 512 !... Must be a power of two.
|
||||
bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
|
||||
kernel_function = 'gaussian'; % You can switch to: 'epanechnikov', 'quartic', 'triangle',
|
||||
% 'triweight', 'uniform' or 'cosinus' kernels (iff bandwidth=0, see posterior_density_estimate.m).
|
||||
truncprior = 10^(-3);
|
||||
|
||||
|
||||
npar=length(xparam1);
|
||||
nruns=length(x2);
|
||||
icol=ceil(sqrt(npar));
|
||||
iraw=icol;
|
||||
if (icol-1)*(icol-2)>=npar
|
||||
iraw = icol-2;
|
||||
icol=icol-1;
|
||||
elseif (icol)*(icol-2)>=npar
|
||||
iraw = icol-2;
|
||||
elseif icol*(icol-1)>=npar
|
||||
iraw=icol-1;
|
||||
end
|
||||
|
||||
pmean=bayestopt_.pmean;
|
||||
pshape=bayestopt_.pshape;
|
||||
p1 = bayestopt_.p1;
|
||||
p2 = bayestopt_.p2;
|
||||
p3 = bayestopt_.p3;
|
||||
p4 = bayestopt_.p4;
|
||||
|
||||
figure('Name',figurename)
|
||||
for i=1:npar;
|
||||
if i<=estim_params_.nvx
|
||||
vname = deblank(M_.exo_name(estim_params_.var_exo(i,1),:));
|
||||
nam=['SE_{',vname,'}'];
|
||||
elseif i<=(estim_params_.nvx+estim_params_.nvn)
|
||||
deblank(options_.varobs(estim_params_.var_endo(i-estim_params_.nvx,1),:));
|
||||
nam=['SE_{EOBS_',vname,'}'];
|
||||
elseif i<=(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx)
|
||||
j = i - (estim_params_.nvx+estim_params_.nvn);
|
||||
k1 = estim_params_.corrx(j,1);
|
||||
k2 = estim_params_.corrx(j,2);
|
||||
vname = [deblank(M_.exo_name(k1,:)) ',' deblank(M_.exo_name(k2,:))];
|
||||
nam=['CC_{',vname,'}'];
|
||||
elseif i<=(estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+ ...
|
||||
estim_params_.ncn)
|
||||
j = i - (estim_params_.nvx+estim_params_.nvn+estim_params_.ncx);
|
||||
k1 = estim_params_.corrn(j,1);
|
||||
k2 = estim_params_.corrn(j,2);
|
||||
vname = [deblank(M_.exo_name(k1,:)) ',' deblank(M_.exo_name(k2,:))];
|
||||
nam=['CC_{EOBS_',vname,'}'];
|
||||
else
|
||||
j = i - (estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+ ...
|
||||
estim_params_.ncn);
|
||||
nam = deblank(estim_params_.param_names(j,:));
|
||||
end
|
||||
subplot(iraw, icol, i);
|
||||
if strcmpi(pltopt,'all'); % Estimation of the density...
|
||||
[abscissa,ff,h] = posterior_density_estimate(x2(round(options_.mh_drop*nruns):end,i),...
|
||||
number_of_grid_points,bandwidth,kernel_function);
|
||||
plot(abscissa,ff,'-k','linewidth',2);
|
||||
end;
|
||||
a = 0;
|
||||
b = 0;
|
||||
if pshape(i) == 1; %/* BETA Prior */
|
||||
density = inline('((1-x).^(b-1)).*x.^(a-1)./beta(a,b)','x','a','b');
|
||||
mu = (p1(i)-p3(i))/(p4(i)-p3(i));
|
||||
stdd = p2(i)/(p4(i)-p3(i));
|
||||
a = (1-mu)*mu^2/stdd^2 - mu;
|
||||
b = a*(1/mu - 1);
|
||||
infbound = qbeta(truncprior,a,b);
|
||||
supbound = qbeta(1-truncprior,a,b);
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = density(abscissa,a,b);
|
||||
abscissa = abscissa*(p4(i)-p3(i))+p3(i);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
elseif pshape(i) == 2; %/* GAMMA PRIOR */
|
||||
% density =
|
||||
% inline('((x/b).^(a-1)).*exp(-x/b)*inv(b*gamma(a))','x','a','b');
|
||||
mu = p1(i)-p3(i);
|
||||
b = p2(i)^2/mu;
|
||||
a = mu/b;
|
||||
infbound = mj_qgamma(truncprior,a)*b;
|
||||
supbound = mj_qgamma(1-truncprior,a)*b;
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = exp(lpdfgam(abscissa,a,b));
|
||||
abscissa = abscissa + p3(i);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
elseif pshape(i) == 3; %/* GAUSSIAN PRIOR */
|
||||
density = inline('inv(sqrt(2*pi)*b)*exp(-0.5*((x-a)/b).^2)','x','a','b');
|
||||
a = p1(i);
|
||||
b = p2(i);
|
||||
infbound = qnorm(truncprior,a,b);
|
||||
supbound = qnorm(1-truncprior,a,b);
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = density(abscissa,a,b);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
elseif pshape(i) == 4; %/* INVGAMMA PRIOR type 1 */
|
||||
density = inline('2*inv(gamma(nu/2))*(x.^(-nu-1))*((s/2)^(nu/2)).*exp(-s./(2*x.^2))','x','s','nu');
|
||||
nu = p2(i);
|
||||
s = p1(i);
|
||||
a = nu/2;
|
||||
b = 2/s;
|
||||
infbound = 1/sqrt(mj_qgamma(1-10*truncprior,a)*b);
|
||||
supbound = 1/sqrt(mj_qgamma(10*truncprior,a)*b);
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = density(abscissa,s,nu);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
elseif pshape(i) == 5; %/* UNIFORM PRIOR */
|
||||
density = inline('(x.^0)/(b-a)','x','a','b');
|
||||
a = p1(i);
|
||||
b = p2(i);
|
||||
infbound = a;
|
||||
supbound = b;
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = density(abscissa,a,b);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
elseif pshape(i) == 6; %/* INVGAMMA PRIOR type 2 */
|
||||
density = inline('inv(gamma(nu/2))*(x.^(-.5(nu+2)))*((s/2)^(nu/2)).*exp(-s./(2*x))','x','s','nu');
|
||||
nu = p2(i);
|
||||
s = p1(i);
|
||||
a = nu/2;
|
||||
b = 2/s;
|
||||
infbound = 1/(qgamma(1-truncprior,a)*b);
|
||||
supbound = 1/(qgamma(truncprior,a)*b);
|
||||
stepsize = (supbound-infbound)/200;
|
||||
abscissa = infbound:stepsize:supbound;
|
||||
f = density(abscissa,s,nu);
|
||||
if strcmp(pltopt,'all');
|
||||
top = max([max(ff);max(f)]);
|
||||
end;
|
||||
end;
|
||||
hold on;
|
||||
k = [1:length(f)];
|
||||
if pshape(i) ~= 5
|
||||
[junk,k1] = max(f);
|
||||
if k1 == 1 | k1 == length(f)
|
||||
k = find(f < 10);
|
||||
end
|
||||
end
|
||||
hh = plot(abscissa(k),f(k),'-k','linewidth',2);
|
||||
set(hh,'color',[0.7 0.7 0.7]);
|
||||
%hold on; %% ?!
|
||||
if strcmp(pltopt,'all');
|
||||
plot( [xparam1(i) xparam1(i)], [0,top], '--g', 'linewidth', 2);
|
||||
end;
|
||||
title(nam,'Interpreter','none');
|
||||
hold off;
|
||||
end;
|
||||
drawnow
|
||||
|
||||
% 12/01/03 MJ adapted from M. Ratto's version
|
||||
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
function f = dlognorm(x,lambda,zeta)
|
||||
%DLOGNORM The log-normal density function
|
||||
%
|
||||
% f = dlognorm(x,lambda,zeta)
|
||||
|
||||
% Copyright (c) Halfdan Grage, Anders Holtsberg
|
||||
|
||||
if any(any(zeta<=0))
|
||||
error('Parameter zeta is wrong')
|
||||
end
|
||||
|
||||
neg = find(x <= 0);
|
||||
[n1,n2] = size(neg);
|
||||
x(neg) = ones(n1,n2);
|
||||
f = dnorm(log(x),lambda,zeta)./x;
|
||||
f(neg) = zeros(n1,n2);
|
||||
11
matlab/dr2.m
11
matlab/dr2.m
|
|
@ -1,11 +0,0 @@
|
|||
% Copyright (C) 2001 Michel Juillard
|
||||
%
|
||||
% function used for dr_algo == 1
|
||||
function ghs2=dr2(ys,dr)
|
||||
global M_
|
||||
|
||||
dr.ys = ys;
|
||||
fh = str2func([M_.fname '_static']);
|
||||
dr.fbias = 2*feval(fh,dr.ys);
|
||||
dr=dr1(dr,0);
|
||||
ghs2 = dr.ghs2;
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
function e = fnorm(p2,p,p1,perc)
|
||||
e = p - pnorm(perc,p1,p2);
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
% Copyright (C) 2001 Michel Juillard
|
||||
%
|
||||
function [x,f,fvec,check]=lnsrch(xold,fold,g,p,stpmax,func,varargin)
|
||||
|
||||
alf = 1e-4 ;
|
||||
tolx = 3.7e-11 ;
|
||||
alam = 1;
|
||||
|
||||
nn = size(xold,1) ;
|
||||
summ = sqrt(sum(p.*p)) ;
|
||||
if ~isfinite(summ)
|
||||
error(['Some element of Newton direction isn''t finite. Jacobian maybe' ...
|
||||
' singular or there is a problem with initial values'])
|
||||
end
|
||||
|
||||
if summ > stpmax
|
||||
p=p.*stpmax/summ ;
|
||||
end
|
||||
|
||||
slope = g'*p ;
|
||||
|
||||
test = max(abs(p)'./max([abs(xold)';ones(1,nn)])) ;
|
||||
alamin = tolx/test ;
|
||||
|
||||
if alamin > 0.1
|
||||
alamin = 0.1;
|
||||
end
|
||||
|
||||
while 1
|
||||
if alam < alamin
|
||||
check = 1 ;
|
||||
return
|
||||
end
|
||||
|
||||
x = xold + (alam*p) ;
|
||||
fvec = feval(func,x,varargin{:}) ;
|
||||
f = 0.5*fvec'*fvec ;
|
||||
|
||||
if any(isnan(fvec))
|
||||
alam = alam/2 ;
|
||||
alam2 = alam ;
|
||||
f2 = f ;
|
||||
fold2 = fold ;
|
||||
else
|
||||
|
||||
if f <= fold+alf*alam*slope
|
||||
check = 0;
|
||||
break ;
|
||||
else
|
||||
if alam == 1
|
||||
tmplam = -slope/(2*(f-fold-slope)) ;
|
||||
else
|
||||
rhs1 = f-fold-alam*slope ;
|
||||
rhs2 = f2-fold2-alam2*slope ;
|
||||
a = (rhs1/(alam^2)-rhs2/(alam2^2))/(alam-alam2) ;
|
||||
b = (-alam2*rhs1/(alam^2)+alam*rhs2/(alam2^2))/(alam-alam2) ;
|
||||
if a == 0
|
||||
tmplam = -slope/(2*b) ;
|
||||
else
|
||||
disc = (b^2)-3*a*slope ;
|
||||
|
||||
if disc < 0
|
||||
error ('Roundoff problem in nlsearch') ;
|
||||
else
|
||||
tmplam = (-b+sqrt(disc))/(3*a) ;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
if tmplam > 0.5*alam
|
||||
tmplam = 0.5*alam;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
alam2 = alam ;
|
||||
f2 = f ;
|
||||
fold2 = fold ;
|
||||
alam = max([tmplam;(0.1*alam)]) ;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% 01/14/01 MJ lnsearch is now a separate function
|
||||
% 01/12/03 MJ check for finite summ to avoid infinite loop when Jacobian
|
||||
% is singular or model is denormalized
|
||||
|
|
@ -1,29 +0,0 @@
|
|||
function y=p2toperc(x)
|
||||
|
||||
n = size(x,1);
|
||||
y = zeros(n,1);
|
||||
|
||||
for i=1:n
|
||||
% if x(i,6) == 0 have to wait until the 2nd part works
|
||||
if 1
|
||||
y(i) = x(i,3);
|
||||
else
|
||||
if x(i,3) < x(i,2)
|
||||
p = 0.05;
|
||||
else
|
||||
p = 0.95;
|
||||
end
|
||||
|
||||
if x(i,1) == 1
|
||||
y(i) = fsolve(@fbeta,1,p,x(i,2),x(i,3));
|
||||
elseif x(i,1) == 2
|
||||
y(i) = fsolve(@fgamma,1,p,x(i,2),x(i,3));
|
||||
elseif x(i,1) == 3
|
||||
y(i) = (x(i,3)-x(i,2))/qnorm(p,0,1);
|
||||
elseif x(i,1) == 4
|
||||
y(i) = fsolve(@figamm,1,p,x(i,2),x(i,3));
|
||||
elseif x(i,1) == 5
|
||||
% y(i) = fsolve(@fgamma,1,p,x(i,2));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
% Copyright (C) 2001 Michel Juillard
|
||||
%
|
||||
function dr=resol1(ys,algo,linear,iorder)
|
||||
|
||||
global M_ options_ oo_ bayestopt_
|
||||
global it_ means_ stderrs_ dr1_test_
|
||||
|
||||
dr1_test_ = zeros(2,1);
|
||||
|
||||
if linear == 1
|
||||
iorder =1;
|
||||
end
|
||||
|
||||
xlen = M_.maximum_lead + M_.maximum_lag + 1;
|
||||
klen = M_.maximum_lag + M_.maximum_lead + 1;
|
||||
iyv = M_.lead_lag_incidence';
|
||||
iyv = iyv(:);
|
||||
iyr0 = find(iyv) ;
|
||||
it_ = M_.maximum_lag + 1 ;
|
||||
|
||||
if M_.exo_nbr == 0
|
||||
oo_.exo_steady_state = [] ;
|
||||
end
|
||||
|
||||
if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
|
||||
error ('RESOL: Error in model specification: some variables don"t appear as current') ;
|
||||
end
|
||||
|
||||
%if xlen > 1
|
||||
% error (['RESOL: stochastic exogenous variables must appear only at the' ...
|
||||
% ' current period. Use additional endogenous variables']) ;
|
||||
%end
|
||||
|
||||
% check if ys is steady state
|
||||
tempex = oo_.exo_simul;
|
||||
oo_.exo_simul = repmat(transpose(oo_.exo_steady_state), M_.maximum_lag + ...
|
||||
M_.maximum_lead+1,1);
|
||||
fh = str2func([M_.fname '_static']);
|
||||
if max(abs(feval(fh,ys,oo_.exo_simul))) > options_.dynatol
|
||||
% dirty trick to call either user function or dynare_solve
|
||||
[dr.ys, cheik] = feval(bayestopt_.static_solve,[M_.fname '_static'],ys,oo_.exo_simul);
|
||||
if cheik
|
||||
dr1_test_(1) = 1; % dynare_solve did not converge to the steady state.
|
||||
resid = feval([M_.fname '_static'],dr.ys,oo_.exo_simul);
|
||||
dr1_test_(2) = resid'*resid; % penalty...
|
||||
disp('dynare_solve is unable to find the steady state.')
|
||||
return
|
||||
end
|
||||
else
|
||||
dr.ys = ys;
|
||||
end
|
||||
|
||||
dr.fbias = zeros(M_.endo_nbr,1);
|
||||
dr = dr11(iorder,dr,0);
|
||||
|
||||
if algo == 1 & iorder > 1
|
||||
dr.ys = dynare_solve('dr2',ys,dr);
|
||||
dr.fbias = 2*feval([M_.fname '_static'],dr.ys,oo_.exo_simul);
|
||||
dr = dr11(iorder,dr,0);
|
||||
end
|
||||
oo_.exo_simul = tempex;
|
||||
tempex = [];
|
||||
|
||||
% 01/01/2003 MJ added dr_algo == 1
|
||||
% 08/24/2001 MJ uses Schmitt-Grohe and Uribe (2001) constant correction
|
||||
% in dr.ghs2
|
||||
% 05/26/2003 MJ added temporary values for oo_.exo_simul
|
||||
% 01/22/2005 SA check --> cheik (to avoid confusion with the function check.m)
|
||||
Loading…
Reference in New Issue