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Merge branch 'cosmetics' of git.dynare.org:JohannesPfeifer/dynare 

Ref. !2165
kalman-mex
Sébastien Villemot 2023-09-11 15:34:52 +02:00
parent dee66e8351 825763b18f
commit 4a0b87fdaf
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GPG Key ID: 2CECE9350ECEBE4A
9 changed files with 56 additions and 42 deletions
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0
matlab/Q6_plication.m → matlab/+pruned_SS/Q6_plication.m
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0
matlab/allVL1.m → matlab/+pruned_SS/allVL1.m
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0
matlab/bivmom.m → matlab/+pruned_SS/bivmom.m
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2
matlab/prodmom.m → matlab/+pruned_SS/prodmom.m
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@ -73,7 +73,7 @@ if m==2
return
end
rho = V(1,2)/sqrt(V(1,1)*V(2,2));
y = V(1,1)^(nu(1)/2)*V(2,2)^(nu(2)/2)*bivmom(nu,rho);
y = V(1,1)^(nu(1)/2)*V(2,2)^(nu(2)/2)*pruned_SS.bivmom(nu,rho);
return
end
%

4
matlab/prodmom_deriv.m → matlab/+pruned_SS/prodmom_deriv.m
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@ -103,13 +103,13 @@ if m==2
rho = V(1,2)/sqrt(V(1,1)*V(2,2));
if nargout > 1
drho = dC(ii(1),ii(2),:);
[tmp,dtmp] = bivmom(nu,rho);
[tmp,dtmp] = pruned_SS.bivmom(nu,rho);
dy = (nu(1)/2)*V(1,1)^(nu(1)/2-1)*dV(1,1,:) * V(2,2)^(nu(2)/2) * tmp...
+ V(1,1)^(nu(1)/2) * (nu(2)/2)*V(2,2)^(nu(2)/2-1)*dV(2,2,:) * tmp...
+ V(1,1)^(nu(1)/2) * V(2,2)^(nu(2)/2) * dtmp * drho;
dy = reshape(dy,1,size(dV,3));
else
tmp = bivmom(nu,rho);
tmp = pruned_SS.bivmom(nu,rho);
end
y = V(1,1)^(nu(1)/2)*V(2,2)^(nu(2)/2)*tmp;
return

0
matlab/quadruplication.m → matlab/+pruned_SS/quadruplication.m
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16
matlab/pruned_state_space_system.m
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@ -424,8 +424,8 @@ if order > 1
%Compute unique fourth order product moments of u, i.e. unique(E[kron(kron(kron(u,u),u),u)],'stable')
u_nbr4 = u_nbr*(u_nbr+1)/2*(u_nbr+2)/3*(u_nbr+3)/4;
if isempty(QPu)
QPu = quadruplication(u_nbr);
COMBOS4 = flipud(allVL1(u_nbr, 4)); %all possible (unique) combinations of powers that sum up to four
QPu = pruned_SS.quadruplication(u_nbr);
COMBOS4 = flipud(pruned_SS.allVL1(u_nbr, 4)); %all possible (unique) combinations of powers that sum up to four
end
E_u_u_u_u = zeros(u_nbr4,1); %only unique entries
if compute_derivs && (stderrparam_nbr+corrparam_nbr>0)
@ -433,9 +433,9 @@ if order > 1
end
for j4 = 1:size(COMBOS4,1)
if compute_derivs && (stderrparam_nbr+corrparam_nbr>0)
[E_u_u_u_u(j4), dE_u_u_u_u(j4,:)] = prodmom_deriv(E_uu, 1:u_nbr, COMBOS4(j4,:), dE_uu(:,:,1:(stderrparam_nbr+corrparam_nbr)), dr.derivs.dCorrelation_matrix(:,:,1:(stderrparam_nbr+corrparam_nbr)));
[E_u_u_u_u(j4), dE_u_u_u_u(j4,:)] = pruned_SS.prodmom_deriv(E_uu, 1:u_nbr, COMBOS4(j4,:), dE_uu(:,:,1:(stderrparam_nbr+corrparam_nbr)), dr.derivs.dCorrelation_matrix(:,:,1:(stderrparam_nbr+corrparam_nbr)));
else
E_u_u_u_u(j4) = prodmom(E_uu, 1:u_nbr, COMBOS4(j4,:));
E_u_u_u_u(j4) = pruned_SS.prodmom(E_uu, 1:u_nbr, COMBOS4(j4,:));
end
end
E_xfxf_uu = kron(E_xfxf,E_uu');
@ -670,8 +670,8 @@ if order > 1
% Compute unique sixth-order product moments of u, i.e. unique(E[kron(kron(kron(kron(kron(u,u),u),u),u),u)],'stable')
u_nbr6 = u_nbr*(u_nbr+1)/2*(u_nbr+2)/3*(u_nbr+3)/4*(u_nbr+4)/5*(u_nbr+5)/6;
if isempty(Q6Pu)
Q6Pu = Q6_plication(u_nbr);
COMBOS6 = flipud(allVL1(u_nbr, 6)); %all possible (unique) combinations of powers that sum up to six
Q6Pu = pruned_SS.Q6_plication(u_nbr);
COMBOS6 = flipud(pruned_SS.allVL1(u_nbr, 6)); %all possible (unique) combinations of powers that sum up to six
end
E_u_u_u_u_u_u = zeros(u_nbr6,1); %only unique entries
if compute_derivs && (stderrparam_nbr+corrparam_nbr>0)
@ -679,9 +679,9 @@ if order > 1
end
for j6 = 1:size(COMBOS6,1)
if compute_derivs && (stderrparam_nbr+corrparam_nbr>0)
[E_u_u_u_u_u_u(j6), dE_u_u_u_u_u_u(j6,:)] = prodmom_deriv(E_uu, 1:u_nbr, COMBOS6(j6,:), dE_uu(:,:,1:(stderrparam_nbr+corrparam_nbr)), dr.derivs.dCorrelation_matrix(:,:,1:(stderrparam_nbr+corrparam_nbr)));
[E_u_u_u_u_u_u(j6), dE_u_u_u_u_u_u(j6,:)] = pruned_SS.prodmom_deriv(E_uu, 1:u_nbr, COMBOS6(j6,:), dE_uu(:,:,1:(stderrparam_nbr+corrparam_nbr)), dr.derivs.dCorrelation_matrix(:,:,1:(stderrparam_nbr+corrparam_nbr)));
else
E_u_u_u_u_u_u(j6) = prodmom(E_uu, 1:u_nbr, COMBOS6(j6,:));
E_u_u_u_u_u_u(j6) = pruned_SS.prodmom(E_uu, 1:u_nbr, COMBOS6(j6,:));
end
end

60
matlab/resol.m
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@ -1,18 +1,18 @@
function [dr, info, M, oo] = resol(check_flag, M, options, oo)
function [dr, info, M_, oo_] = resol(check_flag, M_, options_, oo_)
%[dr, info, M_, oo_] = resol(check_flag, M_, options_, oo_)
% Computes the perturbation based decision rules of the DSGE model (orders 1 to 3)
%
% INPUTS
% - check_flag [integer] scalar, equal to 0 if all the approximation is required, equal to 1 if only the eigenvalues are to be computed.
% - M [structure] Matlab's structure describing the model (M_).
% - options [structure] Matlab's structure describing the current options (options_).
% - oo [structure] Matlab's structure containing the results (oo_).
% - M_ [structure] Matlab's structure describing the model
% - options_ [structure] Matlab's structure describing the current options
% - oo_ [structure] Matlab's structure containing the results
%
% OUTPUTS
% - dr [structure] Reduced form model.
% - info [integer] scalar or vector, error code.
% - M [structure] Matlab's structure describing the model (M_).
% - oo [structure] Matlab's structure containing the results (oo_).
% - M_ [structure] Matlab's structure describing the model
% - oo_ [structure] Matlab's structure containing the results
%
% REMARKS
% Possible values for the error codes are:
@ -49,46 +49,46 @@ function [dr, info, M, oo] = resol(check_flag, M, options, oo)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
if isfield(oo,'dr')
if isfield(oo.dr,'kstate')
dr.kstate = oo.dr.kstate;
if isfield(oo_,'dr')
if isfield(oo_.dr,'kstate')
dr.kstate = oo_.dr.kstate;
end
if isfield(oo.dr,'inv_order_var')
dr.inv_order_var = oo.dr.inv_order_var;
if isfield(oo_.dr,'inv_order_var')
dr.inv_order_var = oo_.dr.inv_order_var;
end
if isfield(oo.dr,'order_var')
dr.order_var = oo.dr.order_var;
if isfield(oo_.dr,'order_var')
dr.order_var = oo_.dr.order_var;
end
if isfield(oo.dr,'restrict_var_list')
dr.restrict_var_list = oo.dr.restrict_var_list;
if isfield(oo_.dr,'restrict_var_list')
dr.restrict_var_list = oo_.dr.restrict_var_list;
end
if isfield(oo.dr,'restrict_columns')
dr.restrict_columns = oo.dr.restrict_columns;
if isfield(oo_.dr,'restrict_columns')
dr.restrict_columns = oo_.dr.restrict_columns;
end
if isfield(oo.dr,'obs_var')
dr.obs_var = oo.dr.obs_var;
if isfield(oo_.dr,'obs_var')
dr.obs_var = oo_.dr.obs_var;
end
end
if M.exo_nbr == 0
oo.exo_steady_state = [] ;
if M_.exo_nbr == 0
oo_.exo_steady_state = [] ;
end
[dr.ys,M.params,info] = evaluate_steady_state(oo.steady_state,[oo.exo_steady_state; oo.exo_det_steady_state],M,options,~options.steadystate.nocheck);
[dr.ys,M_.params,info] = evaluate_steady_state(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_,options_,~options_.steadystate.nocheck);
if info(1)
oo.dr = dr;
oo_.dr = dr;
return
end
if options.loglinear
if options_.loglinear
threshold = 1e-16;
% Find variables with non positive steady state. Skip auxiliary
% variables for lagges/leaded exogenous variables
idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M))<threshold);
idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M_))<threshold);
if ~isempty(idx)
if options.debug
variables_with_non_positive_steady_state = M.endo_names{idx};
if options_.debug
variables_with_non_positive_steady_state = M_.endo_names{idx};
skipline()
fprintf('You are attempting to simulate/estimate a loglinear approximation of a model, but\n')
fprintf('the steady state level of the following variables is not strictly positive:\n')
@ -111,5 +111,5 @@ if options.loglinear
end
end
[dr, info] = stochastic_solvers(dr, check_flag, M, options, oo);
oo.dr = dr;
[dr, info] = stochastic_solvers(dr, check_flag, M_, options_, oo_);
oo_.dr = dr;

16
matlab/stoch_simul.m
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@ -1,6 +1,20 @@
function [info, oo_, options_, M_] = stoch_simul(M_, options_, oo_, var_list)
%[info, oo_, options_, M_] = stoch_simul(M_, options_, oo_, var_list)
% Computes the stochastic simulations
%
% INPUTS
% - M_ [structure] Matlab's structure describing the model
% - options_ [structure] Matlab's structure describing the current options
% - oo_ [structure] Matlab's structure containing the results
% - var_list [character array] list of endogenous variables specified
%
% OUTPUTS
% - info [integer] scalar or vector, error code.
% - oo [structure] Matlab's structure containing the results (oo_).
% - options_ [structure] Matlab's structure describing the current options
% - M [structure] Matlab's structure describing the model (M_).
% Copyright © 2001-2021 Dynare Team
% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%