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Modernization: use tilde (~) syntax for ignored output arguments

time-shift
Sébastien Villemot 2018-11-13 17:58:42 +01:00
parent ce49cd95df
commit 7a75872f72
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GPG Key ID: 2CECE9350ECEBE4A
54 changed files with 131 additions and 133 deletions
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2
matlab/PosteriorIRF_core1.m
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@ -194,7 +194,7 @@ while fpar<B
end end
if MAX_nirfs_dsgevar if MAX_nirfs_dsgevar
IRUN = IRUN+1; IRUN = IRUN+1;
[fval,info,junk1,junk2,junk3,junk3,junk4,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_); [fval,info,~,~,~,~,~,PHI,SIGMAu,iXX] = dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
dsge_prior_weight = M_.params(strmatch('dsge_prior_weight', M_.param_names)); dsge_prior_weight = M_.params(strmatch('dsge_prior_weight', M_.param_names));
DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight)); DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1))); SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));

2
matlab/WriteShockDecomp2Excel.m
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@ -93,7 +93,7 @@ for j=1:nvar
d0={}; d0={};
z1 = squeeze(z(i_var(j),:,:)); z1 = squeeze(z(i_var(j),:,:));
if screen_shocks if screen_shocks
[junk, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend'); [~, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values'); labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values');
zres = sum(z1(isort(17:end),:),1); zres = sum(z1(isort(17:end),:),1);
z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)]; z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)];

4
matlab/annualized_shock_decomposition.m
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@ -128,7 +128,7 @@ if realtime_==0
myopts=options_; myopts=options_;
myopts.plot_shock_decomp.type='qoq'; myopts.plot_shock_decomp.type='qoq';
myopts.plot_shock_decomp.realtime=0; myopts.plot_shock_decomp.realtime=0;
[z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]); [z, ~] = plot_shock_decomposition(M_,oo_,myopts,[]);
else else
z = oo_; z = oo_;
end end
@ -287,7 +287,7 @@ if isstruct(aux)
yaux=aux.y; yaux=aux.y;
end end
[nvar , nterms, junk] = size(z); [nvar, nterms, ~] = size(z);
for j=1:nvar for j=1:nvar
for k =1:nterms for k =1:nterms
ztmp = squeeze(z(j,k,min((t0-3):-4:1):end)); ztmp = squeeze(z(j,k,min((t0-3):-4:1):end));

6
matlab/bksupk.m
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@ -40,7 +40,7 @@ irf = icc1+(options_.periods-1)*ny ;
d1 = zeros(options_.periods*ny,1) ; d1 = zeros(options_.periods*ny,1) ;
ofs = (((options_.periods-1)*ny+1)-1)*jcf*8 ; ofs = (((options_.periods-1)*ny+1)-1)*jcf*8 ;
junk = fseek(fid,ofs,-1) ; [~] = fseek(fid,ofs,-1) ;
c = fread(fid,[jcf,ny],'float64')'; c = fread(fid,[jcf,ny],'float64')';
d1(ir) = c(:,jcf) ; d1(ir) = c(:,jcf) ;
@ -52,7 +52,7 @@ while i <= M_.maximum_lead || i <= options_.periods
irf1 = selif(irf,irf<=options_.periods*ny) ; irf1 = selif(irf,irf<=options_.periods*ny) ;
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ; ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
junk = fseek(fid,ofs,-1) ; [~] = fseek(fid,ofs,-1) ;
c = fread(fid,[jcf,ny],'float64')' ; c = fread(fid,[jcf,ny],'float64')' ;
d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ; d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
@ -64,7 +64,7 @@ end
while i <= options_.periods while i <= options_.periods
ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ; ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
junk = fseek(fid,ofs,-1) ; [~] = fseek(fid,ofs,-1) ;
c = fread(fid,[jcf,ny],'float64')' ; c = fread(fid,[jcf,ny],'float64')' ;
d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ; d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ;

2
matlab/check_list_of_variables.m
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@ -32,7 +32,7 @@ function varlist = check_list_of_variables(options_, M_, varlist)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
% Get uniques % Get uniques
[junk1, junk2, index_uniques] = varlist_indices(varlist, M_.endo_names); [~, ~, index_uniques] = varlist_indices(varlist, M_.endo_names);
varlist = varlist(index_uniques); varlist = varlist(index_uniques);
msg = false; msg = false;

4
matlab/check_posterior_sampler_options.m
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@ -387,7 +387,7 @@ if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
end end
if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix
[junk, invhess] = compute_mh_covariance_matrix; [~, invhess] = compute_mh_covariance_matrix;
posterior_sampler_options.invhess = invhess; posterior_sampler_options.invhess = invhess;
end end
@ -409,7 +409,7 @@ if strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
error('check_posterior_sampler_options:: This error should not occur, please contact developers.') error('check_posterior_sampler_options:: This error should not occur, please contact developers.')
end end
% % % if options_.load_mh_file && options_.use_mh_covariance_matrix, % % % if options_.load_mh_file && options_.use_mh_covariance_matrix,
% % % [junk, invhess] = compute_mh_covariance_matrix; % % % [~, invhess] = compute_mh_covariance_matrix;
% % % posterior_sampler_options.invhess = invhess; % % % posterior_sampler_options.invhess = invhess;
% % % end % % % end
[V1, D]=eig(invhess); [V1, D]=eig(invhess);

4
matlab/discretionary_policy_1.m
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@ -63,7 +63,7 @@ end
%call steady_state_file if present to update parameters %call steady_state_file if present to update parameters
if options_.steadystate_flag if options_.steadystate_flag
% explicit steady state file % explicit steady state file
[junk,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ... [~,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
options_,0); options_,0);
end end
[U,Uy,W] = feval([M_.fname,'.objective.static'],zeros(endo_nbr,1),[], M_.params); [U,Uy,W] = feval([M_.fname,'.objective.static'],zeros(endo_nbr,1),[], M_.params);
@ -129,7 +129,7 @@ iter=1;
for j=1:numel(Indices) for j=1:numel(Indices)
eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);']) eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);'])
if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead) if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
[junk,row,col]=find(lead_lag_incidence(iter,:)); [~,row,col]=find(lead_lag_incidence(iter,:));
eval(['A',Indices{j},'(:,row)=jacobia_(:,col);']) eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
iter=iter+1; iter=iter+1;
end end

4
matlab/dr_block.m
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@ -409,7 +409,7 @@ for i = 1:Size
index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t
index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t
if n_static > 0 if n_static > 0
[Q, junk] = qr(jacob(:,index_s)); [Q, ~] = qr(jacob(:,index_s));
aa = Q'*jacob; aa = Q'*jacob;
else else
aa = jacob; aa = jacob;
@ -476,7 +476,7 @@ for i = 1:Size
if isfield(options_,'indeterminacy_continuity') if isfield(options_,'indeterminacy_continuity')
if options_.indeterminacy_msv == 1 if options_.indeterminacy_msv == 1
[ss,tt,w,q] = qz(E',D'); [ss,tt,w,q] = qz(E',D');
[ss,tt,w,junk] = reorder(ss,tt,w,q); [ss,tt,w,~] = reorder(ss,tt,w,q);
ss = ss'; ss = ss';
tt = tt'; tt = tt';
w = w'; w = w';

2
matlab/draw_prior_density.m
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@ -111,7 +111,7 @@ switch pshape(indx)
end end
if pshape(indx) ~= 5 if pshape(indx) ~= 5
[junk,k1] = max(dens); [~,k1] = max(dens);
if k1 == 1 || k1 == length(dens) if k1 == 1 || k1 == length(dens)
k = find(dens > 10); k = find(dens > 10);
dens(k) = NaN; dens(k) = NaN;

4
matlab/dyn_first_order_solver.m
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@ -124,7 +124,7 @@ if isempty(reorder_jacobian_columns)
nsfwrd)))]; nsfwrd)))];
index_e2 = npred+nboth+(1:nboth); index_e2 = npred+nboth+(1:nboth);
[junk,cols_b] = find(lead_lag_incidence(maximum_lag+1, order_var)); [~,cols_b] = find(lead_lag_incidence(maximum_lag+1, order_var));
reorder_jacobian_columns = [nonzeros(lead_lag_incidence(:,order_var)'); ... reorder_jacobian_columns = [nonzeros(lead_lag_incidence(:,order_var)'); ...
nz+(1:exo_nbr)']; nz+(1:exo_nbr)'];
@ -138,7 +138,7 @@ dr.state_var = state_var;
jacobia = jacobia(:,reorder_jacobian_columns); jacobia = jacobia(:,reorder_jacobian_columns);
if nstatic > 0 if nstatic > 0
[Q, junk] = qr(jacobia(:,index_s)); [Q, ~] = qr(jacobia(:,index_s));
aa = Q'*jacobia; aa = Q'*jacobia;
else else
aa = jacobia; aa = jacobia;

8
matlab/dyn_ramsey_static.m
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@ -75,7 +75,7 @@ elseif options_.steadystate_flag
ys_init(k_inst) = inst_val; ys_init(k_inst) = inst_val;
exo_ss = [oo.exo_steady_state oo.exo_det_steady_state]; exo_ss = [oo.exo_steady_state oo.exo_det_steady_state];
[xx,params] = evaluate_steady_state_file(ys_init,exo_ss,M,options_,~options_.steadystate.nocheck); %run steady state file again to update parameters [xx,params] = evaluate_steady_state_file(ys_init,exo_ss,M,options_,~options_.steadystate.nocheck); %run steady state file again to update parameters
[junk,junk,steady_state] = nl_func(inst_val); %compute and return steady state [~,~,steady_state] = nl_func(inst_val); %compute and return steady state
else else
n_var = M.orig_endo_nbr; n_var = M.orig_endo_nbr;
xx = oo.steady_state(1:n_var); xx = oo.steady_state(1:n_var);
@ -85,7 +85,7 @@ else
if info1~=0 if info1~=0
check=81; check=81;
end end
[junk,junk,steady_state] = nl_func(xx); [~,~,steady_state] = nl_func(xx);
end end
@ -194,8 +194,8 @@ end
function result = check_static_model(ys,M,options_,oo) function result = check_static_model(ys,M,options_,oo)
result = false; result = false;
if (options_.bytecode) if (options_.bytecode)
[chck, res, junk] = bytecode('static',ys,[oo.exo_steady_state oo.exo_det_steady_state], ... [chck, res, ~] = bytecode('static',ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
M.params, 'evaluate'); M.params, 'evaluate');
else else
res = feval([M.fname '.static'],ys,[oo.exo_steady_state oo.exo_det_steady_state], ... res = feval([M.fname '.static'],ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
M.params); M.params);

2
matlab/dyn_risky_steadystate_solver.m
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@ -88,7 +88,7 @@ exo_nbr = M.exo_nbr;
M.var_order_endo_names = M.endo_names(dr.order_var); M.var_order_endo_names = M.endo_names(dr.order_var);
[junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var)); [~,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
dr.i_fwrd_f = i_fwrd_f; dr.i_fwrd_f = i_fwrd_f;
nd = nnz(lead_lag_incidence) + M.exo_nbr; nd = nnz(lead_lag_incidence) + M.exo_nbr;
dr.nd = nd; dr.nd = nd;

4
matlab/dyn_second_order_solver.m
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@ -105,7 +105,7 @@ k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
% Jacobian with respect to the variables with the highest lead % Jacobian with respect to the variables with the highest lead
fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:))); fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
B(:,nstatic+M_.npred+1:end) = fyp; B(:,nstatic+M_.npred+1:end) = fyp;
[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var)); [~,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=... A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred); A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
C = Gy; C = Gy;
@ -162,7 +162,7 @@ kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
E1 = [eye(nspred); zeros(kp-nspred,nspred)]; E1 = [eye(nspred); zeros(kp-nspred,nspred)];
H = E1; H = E1;
hxx = dr.ghxx(nstatic+[1:nspred],:); hxx = dr.ghxx(nstatic+[1:nspred],:);
[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var)); [~,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)'; k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
[B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC); [B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
mexErrCheck('sparse_hessian_times_B_kronecker_C', err); mexErrCheck('sparse_hessian_times_B_kronecker_C', err);

2
matlab/dynare.m
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@ -191,7 +191,7 @@ end
if ispc if ispc
arch = getenv('PROCESSOR_ARCHITECTURE'); arch = getenv('PROCESSOR_ARCHITECTURE');
else else
[junk, arch] = system('uname -m'); [~, arch] = system('uname -m');
end end
if isempty(strfind(arch, '64')) if isempty(strfind(arch, '64'))

9
matlab/dynare_estimation_1.m
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@ -235,8 +235,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood') if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
ana_deriv_old = options_.analytic_derivation; ana_deriv_old = options_.analytic_derivation;
options_.analytic_derivation = 2; options_.analytic_derivation = 2;
[junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ... [~,~,~,~,hh] = feval(objective_function,xparam1, ...
dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_); dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
options_.analytic_derivation = ana_deriv_old; options_.analytic_derivation = ana_deriv_old;
elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood')) elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
% with flag==0, we force to use the hessian from outer product gradient of optimizer 5 % with flag==0, we force to use the hessian from outer product gradient of optimizer 5
@ -373,9 +373,8 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
skipline() skipline()
end end
if options_.dsge_var if options_.dsge_var
[junk1,junk2,junk3,junk4,junk5,junk6,junk7,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =... [~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_); feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
clear('junk1','junk2','junk3','junk4','junk5','junk6','junk7');
end end
elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
@ -519,7 +518,7 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis; oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
end end
end end
[error_flag,junk,options_]= metropolis_draw(1,options_,estim_params_,M_); [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
if options_.bayesian_irf if options_.bayesian_irf
if error_flag if error_flag
error('Estimation::mcmc: I cannot compute the posterior IRFs!') error('Estimation::mcmc: I cannot compute the posterior IRFs!')

22
matlab/dynare_estimation_init.m
View File

@ -454,38 +454,38 @@ if options_.block == 1
% Set restrict_state to postion of observed + state variables in expanded state vector. % Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = [k1(i_posA) M_.state_var(sort(i_posB))]; oo_.dr.restrict_var_list = [k1(i_posA) M_.state_var(sort(i_posB))];
% set mf0 to positions of state variables in restricted state vector for likelihood computation. % set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list); [~,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation. % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list); [~,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing. % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index_dr; bayestopt_.mf2 = var_obs_index_dr;
bayestopt_.mfys = k1; bayestopt_.mfys = k1;
oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))]; oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
[k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows'); [k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))']; bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:)); [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
[junk,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var); [~,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
bayestopt_.smoother_restrict_columns = ic; bayestopt_.smoother_restrict_columns = ic;
[junk,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list); [~,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
else else
% Define union of observed and state variables % Define union of observed and state variables
k2 = union(var_obs_index_dr,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows'); k2 = union(var_obs_index_dr,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
% Set restrict_state to postion of observed + state variables in expanded state vector. % Set restrict_state to postion of observed + state variables in expanded state vector.
oo_.dr.restrict_var_list = k2; oo_.dr.restrict_var_list = k2;
% set mf0 to positions of state variables in restricted state vector for likelihood computation. % set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2); [~,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation. % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(var_obs_index_dr,k2); [~,bayestopt_.mf1] = ismember(var_obs_index_dr,k2);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing. % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index_dr; bayestopt_.mf2 = var_obs_index_dr;
bayestopt_.mfys = k1; bayestopt_.mfys = k1;
[junk,ic] = intersect(k2,nstatic+(1:npred)'); [~,ic] = intersect(k2,nstatic+(1:npred)');
oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)']; oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
bayestopt_.smoother_var_list = union(k2,k3); bayestopt_.smoother_var_list = union(k2,k3);
[junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:)); [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
[junk,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)'); [~,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
bayestopt_.smoother_restrict_columns = ic; bayestopt_.smoother_restrict_columns = ic;
[junk,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list); [~,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list);
end end
if options_.analytic_derivation if options_.analytic_derivation

4
matlab/dynare_gradient.m
View File

@ -52,11 +52,11 @@ for i=1:m
else else
h = H(:,i); h = H(:,i);
end end
[Fh,junk1,junk2,flag] = feval(fcn, x+transpose(h), varargin{:}); [Fh,~,~,flag] = feval(fcn, x+transpose(h), varargin{:});
if flag if flag
G(:,i) = (Fh-F)/epsilon; G(:,i) = (Fh-F)/epsilon;
else else
[Fh,junk1,junk2,flag] = feval(fcn, x-transpose(h), varargin{:}); [Fh,~,~,flag] = feval(fcn, x-transpose(h), varargin{:});
if flag if flag
G(:,i) = (F-Fh)/epsilon; G(:,i) = (F-Fh)/epsilon;
else else

2
matlab/evaluate_steady_state.m
View File

@ -307,7 +307,7 @@ if M.static_and_dynamic_models_differ
z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1); z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1); zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
if options.bytecode if options.bytecode
[chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1); [chck, r, ~]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
mexErrCheck('bytecode', chck); mexErrCheck('bytecode', chck);
elseif options.block elseif options.block
[r, oo.dr] = feval([M.fname '.dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr); [r, oo.dr] = feval([M.fname '.dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);

2
matlab/execute_prior_posterior_function.m
View File

@ -55,7 +55,7 @@ if strcmpi(type,'posterior')
CutSample(M_, options_, estim_params_); CutSample(M_, options_, estim_params_);
%% initialize metropolis draws %% initialize metropolis draws
options_.sub_draws=n_draws; %set draws for sampling; changed value is not returned to base workspace options_.sub_draws=n_draws; %set draws for sampling; changed value is not returned to base workspace
[error_flag,junk,options_]= metropolis_draw(1,options_,estim_params_,M_); [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
if error_flag if error_flag
error('EXECUTE_POSTERIOR_FUNCTION: The draws could not be initialized') error('EXECUTE_POSTERIOR_FUNCTION: The draws could not be initialized')
end end

4
matlab/getH.m
View File

@ -319,8 +319,8 @@ if nargout > 5
end end
end end
[junk,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ... [~,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ...
oo_.dr.order_var)); oo_.dr.order_var));
GAM0 = zeros(M_.endo_nbr,M_.endo_nbr); GAM0 = zeros(M_.endo_nbr,M_.endo_nbr);
Dg0 = zeros(M_.endo_nbr,M_.endo_nbr,param_nbr); Dg0 = zeros(M_.endo_nbr,M_.endo_nbr,param_nbr);
GAM0(:,cols_b) = g1(:,cols_j); GAM0(:,cols_b) = g1(:,cols_j);

2
matlab/get_name_of_the_last_mh_file.m
View File

@ -43,7 +43,7 @@ bk_number = record.Nblck ;
predicted_mhname = [ BaseName '_mh' int2str(mh_number) '_blck' int2str(bk_number) '.mat' ] ; predicted_mhname = [ BaseName '_mh' int2str(mh_number) '_blck' int2str(bk_number) '.mat' ] ;
all_mh_files = dir([BaseName '_mh*_blck*' ]); all_mh_files = dir([BaseName '_mh*_blck*' ]);
[junk,idx] = sort([all_mh_files.datenum]); [~,idx] = sort([all_mh_files.datenum]);
mhname = all_mh_files(idx(end)).name; mhname = all_mh_files(idx(end)).name;
if ~strcmpi(mhname,predicted_mhname) if ~strcmpi(mhname,predicted_mhname)

4
matlab/global_initialization.m
View File

@ -130,8 +130,8 @@ set_dynare_seed('default');
% Create directories % Create directories
[junk,junk]=mkdir(M_.fname); [~,~]=mkdir(M_.fname);
[junk,junk]=mkdir([M_.fname filesep 'Output']); [~,~]=mkdir([M_.fname filesep 'Output']);
% Load user configuration file. % Load user configuration file.
if isfield(options_, 'global_init_file') if isfield(options_, 'global_init_file')

2
matlab/graph_decomp.m
View File

@ -108,7 +108,7 @@ comp_nbr0=comp_nbr;
for j=1:nvar for j=1:nvar
z1 = squeeze(z(i_var(j),:,:)); z1 = squeeze(z(i_var(j),:,:));
if screen_shocks if screen_shocks
[junk, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend'); [~, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values'); labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values');
zres = sum(z1(isort(17:end),:),1); zres = sum(z1(isort(17:end),:),1);
z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)]; z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)];

2
matlab/graph_decomp_detail.m
View File

@ -135,7 +135,7 @@ comp_nbr0=comp_nbr;
for j=1:nvar for j=1:nvar
z1 = squeeze(z(i_var(j),:,:)); z1 = squeeze(z(i_var(j),:,:));
if screen_shocks, if screen_shocks,
[junk, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend'); [~, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values'); labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values');
zres = sum(z1(isort(17:end),:),1); zres = sum(z1(isort(17:end),:),1);
z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)]; z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)];

2
matlab/gsa/prior_draw_gsa.m
View File

@ -49,7 +49,7 @@ if init
pdraw = zeros(npar,1); pdraw = zeros(npar,1);
lbcum = zeros(npar,1); lbcum = zeros(npar,1);
ubcum = ones(npar,1); ubcum = ones(npar,1);
[junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds [~,~,~,lb,ub,~] = set_prior(estim_params_,M_,options_); %Prepare bounds
if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0) if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
% Set prior bounds % Set prior bounds
bounds = prior_bounds(bayestopt_, options_.prior_trunc); bounds = prior_bounds(bayestopt_, options_.prior_trunc);

2
matlab/gsa/stab_map_.m
View File

@ -92,7 +92,7 @@ p2 = bayestopt_.p2(nshock+1:end);
p3 = bayestopt_.p3(nshock+1:end); p3 = bayestopt_.p3(nshock+1:end);
p4 = bayestopt_.p4(nshock+1:end); p4 = bayestopt_.p4(nshock+1:end);
[junk1,junk2,junk3,lb,ub,junk4] = set_prior(estim_params_,M_,options_); %Prepare bounds [~,~,~,lb,ub,~] = set_prior(estim_params_,M_,options_); %Prepare bounds
if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0) if ~isempty(bayestopt_) && any(bayestopt_.pshape > 0)
% Set prior bounds % Set prior bounds
bounds = prior_bounds(bayestopt_, options_.prior_trunc); bounds = prior_bounds(bayestopt_, options_.prior_trunc);

2
matlab/identification_analysis.m
View File

@ -198,7 +198,7 @@ if info(1)==0
if isoctave || matlab_ver_less_than('8.3') if isoctave || matlab_ver_less_than('8.3')
[V,D]=eig(cc); [V,D]=eig(cc);
%fix for older Matlab versions that do not support computing left eigenvalues, see http://mathworks.com/help/releases/R2012b/matlab/ref/eig.html %fix for older Matlab versions that do not support computing left eigenvalues, see http://mathworks.com/help/releases/R2012b/matlab/ref/eig.html
[W,junk] = eig(cc.'); [W,~] = eig(cc.');
W = conj(W); W = conj(W);
else else
[V,D,W]=eig(cc); [V,D,W]=eig(cc);

2
matlab/initial_condition_decomposition.m
View File

@ -74,7 +74,7 @@ end
if ~isfield(oo_,'initval_decomposition') if ~isfield(oo_,'initval_decomposition')
options_.selected_variables_only = 0; %make sure all variables are stored options_.selected_variables_only = 0; %make sure all variables are stored
options_.plot_priors=0; options_.plot_priors=0;
[oo,M,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_); [oo,M,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
% reduced form % reduced form
dr = oo.dr; dr = oo.dr;

4
matlab/initial_estimation_checks.m
View File

@ -66,7 +66,7 @@ if isfield(EstimatedParameters,'param_vals') && ~isempty(EstimatedParameters.par
%check whether steady state file changes estimated parameters %check whether steady state file changes estimated parameters
Model_par_varied=Model; %store Model structure Model_par_varied=Model; %store Model structure
Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters Model_par_varied.params(EstimatedParameters.param_vals(:,1))=Model_par_varied.params(EstimatedParameters.param_vals(:,1))*1.01; %vary parameters
[junk, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0); [~, new_steady_params_2] = evaluate_steady_state(DynareResults.steady_state,Model_par_varied,DynareOptions,DynareResults,DynareOptions.diffuse_filter==0);
changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ... changed_par_indices=find((old_steady_params(EstimatedParameters.param_vals(:,1))-new_steady_params(EstimatedParameters.param_vals(:,1))) ...
| (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1)))); | (Model_par_varied.params(EstimatedParameters.param_vals(:,1))-new_steady_params_2(EstimatedParameters.param_vals(:,1))));
@ -105,7 +105,7 @@ end
% display warning if some parameters are still NaN % display warning if some parameters are still NaN
test_for_deep_parameters_calibration(Model); test_for_deep_parameters_calibration(Model);
[lnprior, junk1,junk2,info]= priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4); [lnprior,~,~,info]= priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
if info if info
fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',BayesInfo.name{info,1}) fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',BayesInfo.name{info,1})
error('The initial value of the prior is -Inf') error('The initial value of the prior is -Inf')

4
matlab/kalman/likelihood/kalman_filter_ss.m
View File

@ -114,9 +114,9 @@ while t <= last
tmp = (a+K*v); tmp = (a+K*v);
if analytic_derivation if analytic_derivation
if analytic_derivation==2 if analytic_derivation==2
[Da,junk,DLIKt,D2a,junk2, Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady,D2a,D2Yss,D2T,[],[]); [Da,~,DLIKt,D2a,~, Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady,D2a,D2Yss,D2T,[],[]);
else else
[Da,junk,DLIKt,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady); [Da,~,DLIKt,Hesst] = computeDLIK(k,tmp,Z,Zflag,v,T,K,[],iF,Da,DYss,DT,[],[],[],notsteady);
end end
DLIK = DLIK + DLIKt; DLIK = DLIK + DLIKt;
if analytic_derivation==2 || asy_hess if analytic_derivation==2 || asy_hess

2
matlab/kalman/likelihood/univariate_kalman_filter_ss.m
View File

@ -153,7 +153,7 @@ while t<=last
end end
if analytic_derivation if analytic_derivation
if analytic_derivation==2 if analytic_derivation==2
[Da,junk,D2a] = univariate_computeDstate(k,a,P,T,Da,DP,DT,[],0,D2a,D2P,D2T); [Da,~,D2a] = univariate_computeDstate(k,a,P,T,Da,DP,DT,[],0,D2a,D2P,D2T);
else else
Da = univariate_computeDstate(k,a,P,T,Da,DP,DT,[],0); Da = univariate_computeDstate(k,a,P,T,Da,DP,DT,[],0);
end end

2
matlab/lmmcp/lmmcp.m
View File

@ -392,7 +392,7 @@ while (k < kmax) && (Psix > eps2)
Fx = Fxnew; Fx = Fxnew;
Phix = Phixnew; Phix = Phixnew;
Psix = Psixnew; Psix = Psixnew;
[junk,DFx] = feval(FUN,x,varargin{:}); [~,DFx] = feval(FUN,x,varargin{:});
DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset); DPhix = DPhi(x,Fx,DFx,lb,ub,lambda1,lambda2,n,Indexset);
DPsix = DPhix'*Phix; DPsix = DPhix'*Phix;
normDPsix = norm(DPsix); normDPsix = norm(DPsix);

4
matlab/load_m_file_data_legacy.m
View File

@ -18,7 +18,7 @@ function o2WysrOISH = load_m_file_data_legacy(datafile, U7ORsJ0vy3) % --*-- Uni
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
cXDHdrXnqo5KwwVpTRuc6OprAW = datafile(1:end-2); cXDHdrXnqo5KwwVpTRuc6OprAW = datafile(1:end-2);
[pathtocXDHdrXnqo5KwwVpTRuc6OprAW,cXDHdrXnqo5KwwVpTRuc6OprAW,junk] = fileparts(cXDHdrXnqo5KwwVpTRuc6OprAW); [pathtocXDHdrXnqo5KwwVpTRuc6OprAW,cXDHdrXnqo5KwwVpTRuc6OprAW,~] = fileparts(cXDHdrXnqo5KwwVpTRuc6OprAW);
if ~isempty(pathtocXDHdrXnqo5KwwVpTRuc6OprAW) if ~isempty(pathtocXDHdrXnqo5KwwVpTRuc6OprAW)
% We need to change directory, first we keep the current directory in memory... % We need to change directory, first we keep the current directory in memory...
@ -38,7 +38,7 @@ if ~isempty(pathtocXDHdrXnqo5KwwVpTRuc6OprAW)
end end
% Clear all the variables except the ones defined in the script. % Clear all the variables except the ones defined in the script.
clear('pathtocXDHdrXnqo5KwwVpTRuc6OprAW', 'cXDHdrXnqo5KwwVpTRuc6OprAW', 'junk'); clear('pathtocXDHdrXnqo5KwwVpTRuc6OprAW', 'cXDHdrXnqo5KwwVpTRuc6OprAW');
% Get the list of variables in the script. % Get the list of variables in the script.
mj6F4eU1BN = whos(); mj6F4eU1BN = whos();

18
matlab/model_diagnostics.m
View File

@ -204,22 +204,22 @@ z = repmat(dr.ys,1,klen);
if ~options.block if ~options.block
if options.order == 1 if options.order == 1
if (options.bytecode) if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ... [chck, ~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
M.params, dr.ys, 1); M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd]; jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else else
[junk,jacobia_] = feval([M.fname '.dynamic'],z(iyr0),exo_simul, ... [~,jacobia_] = feval([M.fname '.dynamic'],z(iyr0),exo_simul, ...
M.params, dr.ys, it_); M.params, dr.ys, it_);
end end
elseif options.order >= 2 elseif options.order >= 2
if (options.bytecode) if (options.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ... [chck, ~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
M.params, dr.ys, 1); M.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x]; jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else else
[junk,jacobia_,hessian1] = feval([M.fname '.dynamic'],z(iyr0),... [~,jacobia_,hessian1] = feval([M.fname '.dynamic'],z(iyr0),...
exo_simul, ... exo_simul, ...
M.params, dr.ys, it_); M.params, dr.ys, it_);
end end
if options.use_dll if options.use_dll
% In USE_DLL mode, the hessian is in the 3-column sparse representation % In USE_DLL mode, the hessian is in the 3-column sparse representation

10
matlab/occbin/get_coef.m
View File

@ -6,19 +6,19 @@ coef_y = zeros(endo_nbr,3*endo_nbr);
coef_u = zeros(endo_nbr,M.exo_nbr); coef_u = zeros(endo_nbr,M.exo_nbr);
if M.maximum_lag > 0 if M.maximum_lag > 0
[junk,c1,c2] = find(ll(1,:)); [~,c1,c2] = find(ll(1,:));
coef_y(:,c1) = jacobian(:,c2); coef_y(:,c1) = jacobian(:,c2);
[junk,c1,c2] = find(ll(2,:)); [~,c1,c2] = find(ll(2,:));
coef_y(:,c1+endo_nbr) = jacobian(:,c2); coef_y(:,c1+endo_nbr) = jacobian(:,c2);
if M.maximum_lead > 0 if M.maximum_lead > 0
[junk,c1,c2] = find(ll(3,:)); [~,c1,c2] = find(ll(3,:));
coef_y(:,c1+2*endo_nbr) = jacobian(:,c2); coef_y(:,c1+2*endo_nbr) = jacobian(:,c2);
end end
else else
[junk,c1,c2] = find(ll(1,:)); [~,c1,c2] = find(ll(1,:));
coef_y(:,c1+endo_nbr) = jacobian(:,c2); coef_y(:,c1+endo_nbr) = jacobian(:,c2);
if M.maximum_lead > 0 if M.maximum_lead > 0
[junk,c1,c2] = find(ll(2,:)); [~,c1,c2] = find(ll(2,:));
coef_y(:,c1+2*endo_nbr) = jacobian(:,c2); coef_y(:,c1+2*endo_nbr) = jacobian(:,c2);
end end
end end

8
matlab/optimization/csminwel1.m
View File

@ -142,7 +142,7 @@ while ~done
elseif ischar(grad) elseif ischar(grad)
[g1, badg1] = grad(x1,varargin{:}); [g1, badg1] = grad(x1,varargin{:});
else else
[junk1,cost_flag,g1] = penalty_objective_function(x1,fcn,penalty,varargin{:}); [~,cost_flag,g1] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg1 = ~cost_flag; badg1 = ~cost_flag;
end end
wall1=badg1; wall1=badg1;
@ -169,7 +169,7 @@ while ~done
elseif ischar(grad) elseif ischar(grad)
[g2, badg2] = grad(x2,varargin{:}); [g2, badg2] = grad(x2,varargin{:});
else else
[junk2,cost_flag,g2] = penalty_objective_function(x1,fcn,penalty,varargin{:}); [~,cost_flag,g2] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg2 = ~cost_flag; badg2 = ~cost_flag;
end end
wall2=badg2; wall2=badg2;
@ -201,7 +201,7 @@ while ~done
elseif ischar(grad) elseif ischar(grad)
[g3, badg3] = grad(x3,varargin{:}); [g3, badg3] = grad(x3,varargin{:});
else else
[junk3,cost_flag,g3] = penalty_objective_function(x1,fcn,penalty,varargin{:}); [~,cost_flag,g3] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badg3 = ~cost_flag; badg3 = ~cost_flag;
end end
wall3=badg3; wall3=badg3;
@ -261,7 +261,7 @@ while ~done
elseif ischar(grad) elseif ischar(grad)
[gh, badgh] = grad(xh,varargin{:}); [gh, badgh] = grad(xh,varargin{:});
else else
[junkh,cost_flag,gh] = penalty_objective_function(x1,fcn,penalty,varargin{:}); [~,cost_flag,gh] = penalty_objective_function(x1,fcn,penalty,varargin{:});
badgh = ~cost_flag; badgh = ~cost_flag;
end end
end end

4
matlab/optimization/numgrad3_.m
View File

@ -46,12 +46,12 @@ for i=1:n
xiold = x(i); xiold = x(i);
h = step_length_correction(xiold,scale,i)*delta; h = step_length_correction(xiold,scale,i)*delta;
x(i) = xiold + h; x(i) = xiold + h;
[f1,junk1,cost_flag1] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f1,~,cost_flag1] = penalty_objective_function(x, fcn, penalty, varargin{:});
if ~cost_flag1 if ~cost_flag1
fprintf('Gradient w.r.t. parameter number %3d (x=%16.8f,+h=%16.8f,f0=%16.8f,f1=%16.8f,f2=%16.8f,g0=%16.8f): penalty on the right!\n',i,xiold,h,f0,f1,f2,(f1 - f2) / (2*h)) fprintf('Gradient w.r.t. parameter number %3d (x=%16.8f,+h=%16.8f,f0=%16.8f,f1=%16.8f,f2=%16.8f,g0=%16.8f): penalty on the right!\n',i,xiold,h,f0,f1,f2,(f1 - f2) / (2*h))
end end
x(i) = xiold - h; x(i) = xiold - h;
[f2,junk2,cost_flag2] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f2,~,cost_flag2] = penalty_objective_function(x, fcn, penalty, varargin{:});
if ~cost_flag2 if ~cost_flag2
fprintf('Gradient w.r.t. parameter number %3d (x=%16.8f,+h=%16.8f,f0=%16.8f,f1=%16.8f,f2=%16.8f,g0=%16.8f): penalty on the left!\n',i,xiold,h,f0,f1,f2,(f1 - f2) / (2*h)) fprintf('Gradient w.r.t. parameter number %3d (x=%16.8f,+h=%16.8f,f0=%16.8f,f1=%16.8f,f2=%16.8f,g0=%16.8f): penalty on the left!\n',i,xiold,h,f0,f1,f2,(f1 - f2) / (2*h))
end end

8
matlab/optimization/numgrad5_.m
View File

@ -48,13 +48,13 @@ for i=1:n
xiold = x(i); xiold = x(i);
h = step_length_correction(xiold,scale,i)*delta; h = step_length_correction(xiold,scale,i)*delta;
x(i) = xiold+h; x(i) = xiold+h;
[f1,junk1,cost_flag1,] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f1,~,cost_flag1,] = penalty_objective_function(x, fcn, penalty, varargin{:});
x(i) = xiold-h; x(i) = xiold-h;
[f2,junk1,cost_flag2] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f2,~,cost_flag2] = penalty_objective_function(x, fcn, penalty, varargin{:});
x(i) = xiold+2*h; x(i) = xiold+2*h;
[f3,junk1,cost_flag3] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f3,~,cost_flag3] = penalty_objective_function(x, fcn, penalty, varargin{:});
x(i) = xiold-2*h; x(i) = xiold-2*h;
[f4,junk1,cost_flag4] = penalty_objective_function(x, fcn, penalty, varargin{:}); [f4,~,cost_flag4] = penalty_objective_function(x, fcn, penalty, varargin{:});
if f0<f1 && f1<f3 && f0<f2 && f2<f4 if f0<f1 && f1<f3 && f0<f2 && f2<f4
g0 = 0; g0 = 0;
else else

6
matlab/optimization/simplex_optimization_routine.m
View File

@ -190,7 +190,7 @@ if verbose
disp('Simplex initialization...') disp('Simplex initialization...')
end end
initial_point = x; initial_point = x;
[initial_score,junk1,nopenalty] = feval(objective_function,x,varargin{:}); [initial_score,~,nopenalty] = feval(objective_function,x,varargin{:});
if ~nopenalty if ~nopenalty
disp('Cannot initialize the simplex with the provided initial guess.') disp('Cannot initialize the simplex with the provided initial guess.')
skipline() skipline()
@ -528,7 +528,7 @@ for j = 1:n
end end
v(:,j+1) = y; v(:,j+1) = y;
x = y; x = y;
[fv(j+1),junk1,nopenalty_flag] = feval(objective_function,x,varargin{:}); [fv(j+1),~,nopenalty_flag] = feval(objective_function,x,varargin{:});
if check_delta if check_delta
while ~nopenalty_flag while ~nopenalty_flag
if y(j)~=0 if y(j)~=0
@ -544,7 +544,7 @@ for j = 1:n
end end
v(:,j+1) = y; v(:,j+1) = y;
x = y; x = y;
[fv(j+1),junk1,nopenalty_flag] = feval(objective_function,x,varargin{:}); [fv(j+1),~,nopenalty_flag] = feval(objective_function,x,varargin{:});
end end
end end
end end

6
matlab/partial_information/dr1_PI.m
View File

@ -91,7 +91,7 @@ if options_.ramsey_policy && options_.ACES_solver == 0
opt.jacobian_flag = 0; opt.jacobian_flag = 0;
oo_.steady_state = dynare_solve('ramsey_static',oo_.steady_state,opt,M_,options_,oo_,it_); oo_.steady_state = dynare_solve('ramsey_static',oo_.steady_state,opt,M_,options_,oo_,it_);
options_.solve_algo = old_solve_algo; options_.solve_algo = old_solve_algo;
[junk,junk,multbar] = ramsey_static(oo_.steady_state,M_,options_,oo_,it_); [~,~,multbar] = ramsey_static(oo_.steady_state,M_,options_,oo_,it_);
[jacobia_,M_] = ramsey_dynamic(oo_.steady_state,multbar,M_,options_,oo_,it_); [jacobia_,M_] = ramsey_dynamic(oo_.steady_state,multbar,M_,options_,oo_,it_);
klen = M_.maximum_lag + M_.maximum_lead + 1; klen = M_.maximum_lag + M_.maximum_lead + 1;
dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar]; dr.ys = [oo_.steady_state;zeros(M_.exo_nbr,1);multbar];
@ -123,14 +123,14 @@ else
lq_instruments.sim_ruleids=sim_ruleids; lq_instruments.sim_ruleids=sim_ruleids;
lq_instruments.tct_ruleids=tct_ruleids; lq_instruments.tct_ruleids=tct_ruleids;
%if isfield(lq_instruments,'xsopt_SS') %% changed by BY %if isfield(lq_instruments,'xsopt_SS') %% changed by BY
[junk, lq_instruments.xsopt_SS,lq_instruments.lmopt_SS,s2,check] = opt_steady_get;%% changed by BY [~, lq_instruments.xsopt_SS,lq_instruments.lmopt_SS,s2,check] = opt_steady_get;%% changed by BY
[qc, DYN_Q] = QPsolve(lq_instruments, s2, check); %% added by BY [qc, DYN_Q] = QPsolve(lq_instruments, s2, check); %% added by BY
z = repmat(lq_instruments.xsopt_SS,1,klen); z = repmat(lq_instruments.xsopt_SS,1,klen);
else else
z = repmat(dr.ys,1,klen); z = repmat(dr.ys,1,klen);
end end
z = z(iyr0) ; z = z(iyr0) ;
[junk,jacobia_] = feval([M_.fname '.dynamic'],z,[oo_.exo_simul ... [~,jacobia_] = feval([M_.fname '.dynamic'],z,[oo_.exo_simul ...
oo_.exo_det_simul], M_.params, dr.ys, it_); oo_.exo_det_simul], M_.params, dr.ys, it_);
if options_.ACES_solver==1 && (length(sim_ruleids)>0 || length(tct_ruleids)>0 ) if options_.ACES_solver==1 && (length(sim_ruleids)>0 || length(tct_ruleids)>0 )

10
matlab/perfect-foresight-models/det_cond_forecast.m
View File

@ -368,7 +368,7 @@ end
save_options_initval_file = options_.initval_file; save_options_initval_file = options_.initval_file;
options_.initval_file = '__'; options_.initval_file = '__';
[pos_constrained_pf, junk] = find(constrained_perfect_foresight); [pos_constrained_pf, ~] = find(constrained_perfect_foresight);
indx_endo_solve_pf = constrained_vars(pos_constrained_pf); indx_endo_solve_pf = constrained_vars(pos_constrained_pf);
if isempty(indx_endo_solve_pf) if isempty(indx_endo_solve_pf)
pf = 0; pf = 0;
@ -572,7 +572,7 @@ else
for t = 1:constrained_periods for t = 1:constrained_periods
if direct_mode && ~isempty(is_constraint) if direct_mode && ~isempty(is_constraint)
[pos_constrained_pf, junk] = find(constrained_perfect_foresight .* is_constraint(t, :)'); [pos_constrained_pf, ~] = find(constrained_perfect_foresight .* is_constraint(t, :)');
indx_endo_solve_pf = constrained_vars(pos_constrained_pf); indx_endo_solve_pf = constrained_vars(pos_constrained_pf);
if isempty(indx_endo_solve_pf) if isempty(indx_endo_solve_pf)
pf = 0; pf = 0;
@ -580,7 +580,7 @@ else
pf = length(indx_endo_solve_pf); pf = length(indx_endo_solve_pf);
end end
[pos_constrained_surprise, junk] = find((1-constrained_perfect_foresight) .* is_constraint(t, :)'); [pos_constrained_surprise, ~] = find((1-constrained_perfect_foresight) .* is_constraint(t, :)');
indx_endo_solve_surprise = constrained_vars(pos_constrained_surprise); indx_endo_solve_surprise = constrained_vars(pos_constrained_surprise);
if isempty(indx_endo_solve_surprise) if isempty(indx_endo_solve_surprise)
@ -591,7 +591,7 @@ else
end end
if direct_mode && ~isempty(is_shock) if direct_mode && ~isempty(is_shock)
[pos_shock_pf, junk] = find(shock_perfect_foresight .* is_shock(t, :)'); [pos_shock_pf, ~] = find(shock_perfect_foresight .* is_shock(t, :)');
indx_endo_solve_pf = shock_vars(pos_shock_pf); indx_endo_solve_pf = shock_vars(pos_shock_pf);
if isempty(indx_endo_solve_pf) if isempty(indx_endo_solve_pf)
b_pf = 0; b_pf = 0;
@ -599,7 +599,7 @@ else
b_pf = length(indx_endo_solve_pf); b_pf = length(indx_endo_solve_pf);
end end
[pos_shock_surprise, junk] = find((1-shock_perfect_foresight) .* is_shock(t, :)'); [pos_shock_surprise, ~] = find((1-shock_perfect_foresight) .* is_shock(t, :)');
indx_endo_solve_surprise = shock_vars(pos_shock_surprise); indx_endo_solve_surprise = shock_vars(pos_shock_surprise);
if isempty(indx_endo_solve_surprise) if isempty(indx_endo_solve_surprise)

4
matlab/perfect-foresight-models/perfect_foresight_solver.m
View File

@ -179,9 +179,9 @@ if ~isreal(oo_.endo_simul(:)) %can only happen without bytecode
yT = real(oo_.endo_simul(:,options_.periods+2)); yT = real(oo_.endo_simul(:,options_.periods+2));
yy = real(oo_.endo_simul(:,2:options_.periods+1)); yy = real(oo_.endo_simul(:,2:options_.periods+1));
illi = M_.lead_lag_incidence'; illi = M_.lead_lag_incidence';
[i_cols,junk,i_cols_j] = find(illi(:)); [i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3); illi = illi(:,2:3);
[i_cols_J1,junk,i_cols_1] = find(illi(:)); [i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)'); i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ... residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ... oo_.exo_simul,M_.params,oo_.steady_state, ...

6
matlab/perfect-foresight-models/perfect_foresight_solver_core.m
View File

@ -123,16 +123,16 @@ if nargout>1
yy = oo_.endo_simul(:,2:options_.periods+1); yy = oo_.endo_simul(:,2:options_.periods+1);
if ~exist('illi') if ~exist('illi')
illi = M_.lead_lag_incidence'; illi = M_.lead_lag_incidence';
[i_cols,junk,i_cols_j] = find(illi(:)); [i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3); illi = illi(:,2:3);
[i_cols_J1,junk,i_cols_1] = find(illi(:)); [i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)'); i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
end end
if options_.block && ~options_.bytecode if options_.block && ~options_.bytecode
maxerror = oo_.deterministic_simulation.error; maxerror = oo_.deterministic_simulation.error;
else else
if options_.bytecode if options_.bytecode
[chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1); [chck, residuals, ~]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1);
else else
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ... residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ... oo_.exo_simul,M_.params,oo_.steady_state, ...

4
matlab/perfect-foresight-models/private/initialize_stacked_problem.m
View File

@ -71,8 +71,8 @@ y0 = endogenousvariables(:,M.maximum_lag);
yT = endogenousvariables(:,M.maximum_lag+periods+1); yT = endogenousvariables(:,M.maximum_lag+periods+1);
z = endogenousvariables(:,M.maximum_lag+(1:periods)); z = endogenousvariables(:,M.maximum_lag+(1:periods));
illi = M.lead_lag_incidence'; illi = M.lead_lag_incidence';
[i_cols, junk,i_cols_j] = find(illi(:)); [i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3); illi = illi(:,2:3);
[i_cols_J1, junk,i_cols_1] = find(illi(:)); [i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M.lead_lag_incidence(1:2,:)'); i_cols_T = nonzeros(M.lead_lag_incidence(1:2,:)');
dynamicmodel = str2func([M.fname,'.dynamic']); dynamicmodel = str2func([M.fname,'.dynamic']);

6
matlab/perfect-foresight-models/private/simulation_core.m
View File

@ -99,16 +99,16 @@ if nargout>1
yy = oo_.endo_simul(:,2:options_.periods+1); yy = oo_.endo_simul(:,2:options_.periods+1);
if ~exist('illi') if ~exist('illi')
illi = M_.lead_lag_incidence'; illi = M_.lead_lag_incidence';
[i_cols,junk,i_cols_j] = find(illi(:)); [i_cols,~,i_cols_j] = find(illi(:));
illi = illi(:,2:3); illi = illi(:,2:3);
[i_cols_J1,junk,i_cols_1] = find(illi(:)); [i_cols_J1,~,i_cols_1] = find(illi(:));
i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)'); i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
end end
if options_.block && ~options_.bytecode if options_.block && ~options_.bytecode
maxerror = oo_.deterministic_simulation.error; maxerror = oo_.deterministic_simulation.error;
else else
if options_.bytecode if options_.bytecode
[chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1); [chck, residuals, ~]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1);
else else
residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ... residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '.dynamic']), y0, yT, ...
oo_.exo_simul,M_.params,oo_.steady_state, ... oo_.exo_simul,M_.params,oo_.steady_state, ...

2
matlab/prior_posterior_statistics_core.m
View File

@ -204,7 +204,7 @@ for b=fpar:B
if run_smoother if run_smoother
[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_); [dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
[alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,junk1,junk2,P,junk4,junk5,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = ... [alphahat,etahat,epsilonhat,alphatilde,SteadyState,trend_coeff,aK,~,~,P,~,~,trend_addition,state_uncertainty,M_,oo_,options_,bayestopt_] = ...
DsgeSmoother(deep,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_); DsgeSmoother(deep,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
stock_trend_coeff(options_.varobs_id,irun(9))=trend_coeff; stock_trend_coeff(options_.varobs_id,irun(9))=trend_coeff;

9
matlab/realtime_shock_decomposition.m
View File

@ -98,8 +98,7 @@ nobs = options_.nobs;
if forecast_ && any(forecast_params) if forecast_ && any(forecast_params)
M1=M_; M1=M_;
M1.params = forecast_params; M1.params = forecast_params;
[junk1,junk2,junk3,junk4,junk5,junk6,oo1] = dynare_resolve(M1,options_,oo_); [~,~,~,~,~,~,oo1] = dynare_resolve(M1,options_,oo_);
clear junk1 junk2 junk3 junk4 junk5 junk6
end end
if fast_realtime if fast_realtime
@ -109,7 +108,7 @@ if fast_realtime
newString=sprintf(running_text); newString=sprintf(running_text);
fprintf(['%s'],newString); fprintf(['%s'],newString);
options_.nobs=fast_realtime; options_.nobs=fast_realtime;
[oo0,M_,junk1,junk2,Smoothed_Variables_deviation_from_mean0] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_); [oo0,M_,~,~,Smoothed_Variables_deviation_from_mean0] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
gend0 = size(oo0.SmoothedShocks.(M_.exo_names{1}),1); gend0 = size(oo0.SmoothedShocks.(M_.exo_names{1}),1);
prctdone=0.5; prctdone=0.5;
if isoctave if isoctave
@ -120,7 +119,7 @@ if fast_realtime
fprintf([s0,'%s'],newString); fprintf([s0,'%s'],newString);
end end
options_.nobs=nobs; options_.nobs=nobs;
[oo2,M_,junk1,junk2,Smoothed_Variables_deviation_from_mean2] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_); [oo2,M_,~,~,Smoothed_Variables_deviation_from_mean2] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
gend2 = size(oo2.SmoothedShocks.(M_.exo_names{1}),1); gend2 = size(oo2.SmoothedShocks.(M_.exo_names{1}),1);
prctdone=1; prctdone=1;
if isoctave if isoctave
@ -142,7 +141,7 @@ for j=presample+1:nobs
% evalin('base',['options_.nobs=' int2str(j) ';']) % evalin('base',['options_.nobs=' int2str(j) ';'])
options_.nobs=j; options_.nobs=j;
if ~fast_realtime if ~fast_realtime
[oo,M_,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_); [oo,M_,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
gend = size(oo.SmoothedShocks.(M_.exo_names{1}),1); gend = size(oo.SmoothedShocks.(M_.exo_names{1}),1);
else else
gend = gend0+j-fast_realtime; gend = gend0+j-fast_realtime;

2
matlab/shock_decomposition.m
View File

@ -72,7 +72,7 @@ end
options_.selected_variables_only = 0; %make sure all variables are stored options_.selected_variables_only = 0; %make sure all variables are stored
options_.plot_priors=0; options_.plot_priors=0;
[oo_, M_, junk1, junk2, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set, varlist, M_, oo_, options_, bayestopt_, estim_params_); [oo_, M_, ~, ~, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set, varlist, M_, oo_, options_, bayestopt_, estim_params_);
% reduced form % reduced form
dr = oo_.dr; dr = oo_.dr;

2
matlab/simulated_moments_estimation.m
View File

@ -97,7 +97,7 @@ if nargin>2
if ~isunix if ~isunix
error('The parallel version of SMM estimation is not implemented for non unix platforms!') error('The parallel version of SMM estimation is not implemented for non unix platforms!')
end end
[junk,hostname] = unix('hostname --fqdn'); [~,hostname] = unix('hostname --fqdn');
hostname = deblank(hostname); hostname = deblank(hostname);
master_is_running_a_job = 0; master_is_running_a_job = 0;
for i=1:length(parallel) for i=1:length(parallel)

24
matlab/stochastic_solvers.m
View File

@ -111,22 +111,22 @@ it_ = M_.maximum_lag + 1;
z = repmat(dr.ys,1,klen); z = repmat(dr.ys,1,klen);
if local_order == 1 if local_order == 1
if (options_.bytecode) if (options_.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ... [chck, ~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
M_.params, dr.ys, 1); M_.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd]; jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
else else
[junk,jacobia_] = feval([M_.fname '.dynamic'],z(iyr0),exo_simul, ... [~,jacobia_] = feval([M_.fname '.dynamic'],z(iyr0),exo_simul, ...
M_.params, dr.ys, it_); M_.params, dr.ys, it_);
end end
elseif local_order == 2 elseif local_order == 2
if (options_.bytecode) if (options_.bytecode)
[chck, junk, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ... [chck, ~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
M_.params, dr.ys, 1); M_.params, dr.ys, 1);
jacobia_ = [loc_dr.g1 loc_dr.g1_x]; jacobia_ = [loc_dr.g1 loc_dr.g1_x];
else else
[junk,jacobia_,hessian1] = feval([M_.fname '.dynamic'],z(iyr0),... [~,jacobia_,hessian1] = feval([M_.fname '.dynamic'],z(iyr0),...
exo_simul, ... exo_simul, ...
M_.params, dr.ys, it_); M_.params, dr.ys, it_);
end end
if options_.use_dll if options_.use_dll
% In USE_DLL mode, the hessian is in the 3-column sparse representation % In USE_DLL mode, the hessian is in the 3-column sparse representation
@ -217,15 +217,15 @@ nz = nnz(M_.lead_lag_incidence);
sdyn = M_.endo_nbr - nstatic; sdyn = M_.endo_nbr - nstatic;
[junk,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ... [~,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ...
order_var)); order_var));
b = zeros(M_.endo_nbr,M_.endo_nbr); b = zeros(M_.endo_nbr,M_.endo_nbr);
b(:,cols_b) = jacobia_(:,cols_j); b(:,cols_b) = jacobia_(:,cols_j);
if M_.maximum_endo_lead == 0 if M_.maximum_endo_lead == 0
% backward models: simplified code exist only at order == 1 % backward models: simplified code exist only at order == 1
if local_order == 1 if local_order == 1
[k1,junk,k2] = find(kstate(:,4)); [k1,~,k2] = find(kstate(:,4));
dr.ghx(:,k1) = -b\jacobia_(:,k2); dr.ghx(:,k1) = -b\jacobia_(:,k2);
if M_.exo_nbr if M_.exo_nbr
dr.ghu = -b\jacobia_(:,nz+1:end); dr.ghu = -b\jacobia_(:,nz+1:end);

2
matlab/store_smoother_results.m
View File

@ -204,7 +204,7 @@ if ~isempty(options_.nk) && options_.nk ~= 0
end end
else else
positions_in_declaration_order=oo_.dr.order_var(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list)); positions_in_declaration_order=oo_.dr.order_var(bayestopt_.smoother_var_list(bayestopt_.smoother_saved_var_list));
[junk,sorted_index_declaration_order]=sort(positions_in_declaration_order); [~,sorted_index_declaration_order]=sort(positions_in_declaration_order);
oo_.FilteredVariablesKStepAhead(:,sorted_index_declaration_order,:)=oo_.FilteredVariablesKStepAhead; oo_.FilteredVariablesKStepAhead(:,sorted_index_declaration_order,:)=oo_.FilteredVariablesKStepAhead;
if ~isempty(PK) && options_.filter_covariance %get K-step ahead variances if ~isempty(PK) && options_.filter_covariance %get K-step ahead variances
oo_.FilteredVariablesKStepAheadVariances(:,sorted_index_declaration_order,sorted_index_declaration_order,:)=oo_.FilteredVariablesKStepAheadVariances; oo_.FilteredVariablesKStepAheadVariances(:,sorted_index_declaration_order,sorted_index_declaration_order,:)=oo_.FilteredVariablesKStepAheadVariances;

2
matlab/user_has_matlab_license.m
View File

@ -31,7 +31,7 @@ function [hasLicense] = user_has_matlab_license(toolbox)
if matlab_ver_less_than('7.12') if matlab_ver_less_than('7.12')
hasLicense = license('test', toolbox); hasLicense = license('test', toolbox);
else else
[hasLicense, junk] = license('checkout',toolbox); [hasLicense, ~] = license('checkout',toolbox);
end end
if ~hasLicense if ~hasLicense
return return

2
matlab/utilities/general/clean_current_folder.m
View File

@ -21,7 +21,7 @@ a = dir('*.mod');
for i = 1:length(a) for i = 1:length(a)
[junk,basename,extension] = fileparts(a(i).name); [~,basename,extension] = fileparts(a(i).name);
if exist([basename '.m']) if exist([basename '.m'])
delete([basename '.m']); delete([basename '.m']);
end end

2
matlab/varlist_indices.m
View File

@ -48,7 +48,7 @@ if ~all(check)
end end
nvar = length(i_var); nvar = length(i_var);
[i_var_unique, index_uniques, junk] = unique(i_var, 'first'); [i_var_unique, index_uniques, ~] = unique(i_var, 'first');
index_uniques = sort(index_uniques); index_uniques = sort(index_uniques);
i_var_unique = i_var(index_uniques); i_var_unique = i_var(index_uniques);