Further naming consistency improvements
parent
879d92fbf8
commit
b3ce518433
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@ -1,5 +1,5 @@
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function write(DynareModel)
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function write(M_)
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% write(M_)
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% Writes the nonlinear problem to be solved for computing the growth
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% rates and levels along the Balanced Growth Path. Note that for
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% the variables growing along the BGP, the identified levels are
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@ -7,7 +7,7 @@ function write(DynareModel)
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% sharing the same trend(s) are relevant.
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%
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% INPUTS
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% - DynareModel [struct] Dynare generated stucture describing the model (M_).
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% - M_ [struct] Dynare generated stucture describing the model (M_).
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%
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% OUTPUTS
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% None
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@ -15,7 +15,7 @@ function write(DynareModel)
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% REMARKS
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% - The trends are assumed to be multiplicative.
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% Copyright © 2019 Dynare Team
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% Copyright © 2019-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -32,18 +32,18 @@ function write(DynareModel)
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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if DynareModel.maximum_lag && ~DynareModel.maximum_lead
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i0 = transpose(DynareModel.lead_lag_incidence(1,:)); % Indices of the lagged variables.
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i1 = transpose(DynareModel.lead_lag_incidence(2,:)); % Indices of the current variables.
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if M_.maximum_lag && ~M_.maximum_lead
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i0 = transpose(M_.lead_lag_incidence(1,:)); % Indices of the lagged variables.
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i1 = transpose(M_.lead_lag_incidence(2,:)); % Indices of the current variables.
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i2 = []; % Indices of the leaded variables.
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elseif DynareModel.maximum_lag && DynareModel.maximum_lead
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i0 = transpose(DynareModel.lead_lag_incidence(1,:)); % Indices of the lagged variables.
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i1 = transpose(DynareModel.lead_lag_incidence(2,:)); % Indices of the current variables.
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i2 = transpose(DynareModel.lead_lag_incidence(3,:)); % Indices of the leaded variables.
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elseif ~DynareModel.maximum_lag && DynareModel.maximum_lead
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elseif M_.maximum_lag && M_.maximum_lead
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i0 = transpose(M_.lead_lag_incidence(1,:)); % Indices of the lagged variables.
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i1 = transpose(M_.lead_lag_incidence(2,:)); % Indices of the current variables.
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i2 = transpose(M_.lead_lag_incidence(3,:)); % Indices of the leaded variables.
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elseif ~M_.maximum_lag && M_.maximum_lead
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i0 = []; % Indices of the lagged variables.
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i1 = transpose(DynareModel.lead_lag_incidence(1,:)); % Indices of the current variables.
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i2 = transpose(DynareModel.lead_lag_incidence(2,:)); % Indices of the leaded variables.
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i1 = transpose(M_.lead_lag_incidence(1,:)); % Indices of the current variables.
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i2 = transpose(M_.lead_lag_incidence(2,:)); % Indices of the leaded variables.
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else
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error('The model is static. The BGP is trivial.')
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end
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@ -71,7 +71,7 @@ else
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end
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% Create function in mod namespace.
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fid = fopen(sprintf('+%s/bgpfun.m', DynareModel.fname), 'w');
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fid = fopen(sprintf('+%s/bgpfun.m', M_.fname), 'w');
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% Write header.
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fprintf(fid, 'function [F, JAC] = bgpfun(z)\n\n');
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@ -80,8 +80,8 @@ fprintf(fid, '%% This file has been generated by dynare (%s).\n\n', datestr(now)
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% The function admits a unique vector as input argument. The first
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% half of the elements are for the levels of the endogenous
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% variables, the second half for the growth factors.
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fprintf(fid, 'y = z(1:%u);\n\n', DynareModel.endo_nbr);
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fprintf(fid, 'g = z(%u:%u);\n', DynareModel.endo_nbr+1, 2*DynareModel.endo_nbr);
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fprintf(fid, 'y = z(1:%u);\n\n', M_.endo_nbr);
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fprintf(fid, 'g = z(%u:%u);\n', M_.endo_nbr+1, 2*M_.endo_nbr);
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% Define the point where the dynamic model is to be evaluated.
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fprintf(fid, 'Y = zeros(%u, 1);\n', 2*(n0+n1+n2));
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@ -118,39 +118,39 @@ end
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fprintf(fid, '\n');
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% Define the vector of parameters.
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fprintf(fid, 'p = zeros(%u, 1);\n', DynareModel.param_nbr);
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for i = 1:DynareModel.param_nbr
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fprintf(fid, 'p(%u) = %16.12f;\n', i, DynareModel.params(i));
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fprintf(fid, 'p = zeros(%u, 1);\n', M_.param_nbr);
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for i = 1:M_.param_nbr
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fprintf(fid, 'p(%u) = %16.12f;\n', i, M_.params(i));
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end
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fprintf(fid, '\n');
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% Initialize the vector holding the residuals over the two periods.
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fprintf(fid, 'F = NaN(%u, 1);\n', 2*DynareModel.endo_nbr);
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fprintf(fid, 'F = NaN(%u, 1);\n', 2*M_.endo_nbr);
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% Set vector of exogenous variables to 0.
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fprintf(fid, 'x = zeros(1, %u);\n\n', DynareModel.exo_nbr);
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fprintf(fid, 'x = zeros(1, %u);\n\n', M_.exo_nbr);
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% Evaluate the residuals and jacobian of the dynamic model in periods t and t+1.
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fprintf(fid, 'if nargout>1\n');
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fprintf(fid, ' J = zeros(%u, %u);\n', 2*DynareModel.endo_nbr, n0+n1+n2+DynareModel.endo_nbr);
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fprintf(fid, ' [F(1:%u), tmp] = %s.dynamic(Y(1:%u), x, p, y, 1);\n', DynareModel.endo_nbr, DynareModel.fname, n0+n1+n2);
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fprintf(fid, ' J(1:%u,1:%u) = tmp(:,1:%u);\n', DynareModel.endo_nbr, n0+n1+n2, n0+n1+n2);
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fprintf(fid, ' [F(%u:%u), tmp] = %s.dynamic(Y(1+%u:%u), x, p, y, 1);\n', DynareModel.endo_nbr+1, 2*DynareModel.endo_nbr, DynareModel.fname, n0+n1+n2, 2*(n0+n1+n2));
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fprintf(fid, ' J(%u:%u,1:%u) = tmp(:,1:%u);\n', DynareModel.endo_nbr+1, 2*DynareModel.endo_nbr, n0+n1+n2, n0+n1+n2);
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fprintf(fid, ' J = zeros(%u, %u);\n', 2*M_.endo_nbr, n0+n1+n2+M_.endo_nbr);
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fprintf(fid, ' [F(1:%u), tmp] = %s.dynamic(Y(1:%u), x, p, y, 1);\n', M_.endo_nbr, M_.fname, n0+n1+n2);
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fprintf(fid, ' J(1:%u,1:%u) = tmp(:,1:%u);\n', M_.endo_nbr, n0+n1+n2, n0+n1+n2);
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fprintf(fid, ' [F(%u:%u), tmp] = %s.dynamic(Y(1+%u:%u), x, p, y, 1);\n', M_.endo_nbr+1, 2*M_.endo_nbr, M_.fname, n0+n1+n2, 2*(n0+n1+n2));
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fprintf(fid, ' J(%u:%u,1:%u) = tmp(:,1:%u);\n', M_.endo_nbr+1, 2*M_.endo_nbr, n0+n1+n2, n0+n1+n2);
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fprintf(fid, 'else\n');
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fprintf(fid, ' F(1:%u) = %s.dynamic(Y(1:%u), x, p, y, 1);\n', DynareModel.endo_nbr, DynareModel.fname, n0+n1+n2);
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fprintf(fid, ' F(%u:%u) = %s.dynamic(Y(1+%u:%u), x, p, y, 1);\n', DynareModel.endo_nbr+1, 2*DynareModel.endo_nbr, DynareModel.fname, n0+n1+n2, 2*(n0+n1+n2));
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fprintf(fid, ' F(1:%u) = %s.dynamic(Y(1:%u), x, p, y, 1);\n', M_.endo_nbr, M_.fname, n0+n1+n2);
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fprintf(fid, ' F(%u:%u) = %s.dynamic(Y(1+%u:%u), x, p, y, 1);\n', M_.endo_nbr+1, 2*M_.endo_nbr, M_.fname, n0+n1+n2, 2*(n0+n1+n2));
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fprintf(fid, 'end\n\n');
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% Compute the jacobian if required.
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fprintf(fid, 'if nargout>1\n');
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fprintf(fid, ' JAC = zeros(%u,%u);\n', 2*DynareModel.endo_nbr, 2*DynareModel.endo_nbr);
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fprintf(fid, ' JAC = zeros(%u,%u);\n', 2*M_.endo_nbr, 2*M_.endo_nbr);
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% Compute the derivatives of the first block of equations (period t)
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% with respect to the endogenous variables.
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if purely_backward_model || purely_forward_model
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for i=1:DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=1:M_.eq_nbr
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for j=1:M_.endo_nbr
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if I1(j)
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if I0(j)
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fprintf(fid, ' JAC(%u,%u) = J(%u,%u)+J(%u,%u)*g(%u);\n', i, j, i, I0(j), i, I1(j), j);
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@ -165,8 +165,8 @@ if purely_backward_model || purely_forward_model
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end
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end
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else
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for i=1:DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=1:M_.eq_nbr
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for j=1:M_.endo_nbr
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if I2(j)
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if I1(j)
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if I0(j)
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@ -201,8 +201,8 @@ end
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% Compute the derivatives of the second block of equations (period t+1)
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% with respect to the endogenous variables.
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if purely_backward_model || purely_forward_model
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for i=DynareModel.eq_nbr+1:2*DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=M_.eq_nbr+1:2*M_.eq_nbr
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for j=1:M_.endo_nbr
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if I1(j)
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if I0(j)
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fprintf(fid, ' JAC(%u,%u) = J(%u,%u)*g(%u)+J(%u,%u)*g(%u)*g(%u);\n', i, j, i, I0(j), j, i, I1(j), j, j);
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@ -217,8 +217,8 @@ if purely_backward_model || purely_forward_model
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end
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end
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else
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for i=DynareModel.eq_nbr+1:2*DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=M_.eq_nbr+1:2*M_.eq_nbr
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for j=1:M_.endo_nbr
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if I2(j)
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if I1(j)
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if I0(j)
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@ -253,16 +253,16 @@ end
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% Compute the derivatives of the first block of equations (period t)
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% with respect to the growth factors.
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if purely_backward_model || purely_forward_model
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for i=1:DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=1:M_.eq_nbr
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for j=1:M_.endo_nbr
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if I1(j)
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fprintf(fid, ' J(%u,%u) = J(%u,%u)*y(%u);\n', i, n0+n1+n2+j, i, I1(j), j);
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end
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end
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end
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else
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for i=1:DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=1:M_.eq_nbr
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for j=1:M_.endo_nbr
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if I2(j)
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if I1(j)
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fprintf(fid, ' J(%u,%u) = J(%u,%u)*y(%u)+J(%u,%u)*2*g(%u)*y(%u);\n', i, n0+n1+n2+j, i, I1(j), j, i, I2(j), j, j);
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@ -281,8 +281,8 @@ end
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% Compute the derivatives of the second block of equations (period t+1)
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% with respect to the endogenous variables.
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if purely_backward_model || purely_forward_model
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for i=DynareModel.eq_nbr+1:2*DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=M_.eq_nbr+1:2*M_.eq_nbr
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for j=1:M_.endo_nbr
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if I0(j)
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fprintf(fid, ' J(%u,%u) = J(%u,%u)+J(%u,%u)*y(%u);\n', i, n0+n1+n2+j, i, n0+n1+n2+j, i, I0(j), j);
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end
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end
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end
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else
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for i=DynareModel.eq_nbr+1:2*DynareModel.eq_nbr
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for j=1:DynareModel.endo_nbr
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for i=M_.eq_nbr+1:2*M_.eq_nbr
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for j=1:M_.endo_nbr
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if I2(j)
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if I1(j)
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if I0(j)
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@ -325,7 +325,7 @@ else
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end
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end
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fprintf(fid, ' JAC(:,%u:%u) = J(:,%u:%u);\n', DynareModel.endo_nbr+1, 2*DynareModel.endo_nbr, n0+n1+n2+1, n0+n1+n2+DynareModel.endo_nbr);
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fprintf(fid, ' JAC(:,%u:%u) = J(:,%u:%u);\n', M_.endo_nbr+1, 2*M_.endo_nbr, n0+n1+n2+1, n0+n1+n2+M_.endo_nbr);
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fprintf(fid,'end\n');
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fclose(fid);
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@ -554,7 +554,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
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[xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds);
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% compute standard errors at mode
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options_mom_.mom.vector_output = false; % make sure flag is reset
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M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and DynareResults (in particular to get oo_.mom.model_moments)
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M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and oo_ (in particular to get oo_.mom.model_moments)
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if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
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options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
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end
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@ -1,11 +1,11 @@
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function DynareModel = parameters(pacname, DynareModel, DynareOutput, verbose)
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function M_ = parameters(pacname, M_, oo_, verbose)
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% M_ = parameters(pacname, M_, oo_, verbose)
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% Updates the parameters of a PAC equation.
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%
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% INPUTS
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% - pacname [string] Name of the pac equation.
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% - DynareModel [struct] M_ global structure (model properties)
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% - DynareOutput [struct] oo_ global structure (model results)
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% - M_ [struct] M_ global structure (model properties)
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% - oo_ [struct] oo_ global structure (model results)
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%
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% OUTPUTS
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% - none
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@ -13,7 +13,7 @@ function DynareModel = parameters(pacname, DynareModel, DynareOutput, verbose)
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2018-2021 Dynare Team
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% Copyright © 2018-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -40,27 +40,27 @@ if ~isrow(pacname)==1 || ~ischar(pacname)
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end
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% Check the name of the PAC model.
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if ~isfield(DynareModel.pac, pacname)
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if ~isfield(M_.pac, pacname)
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error('PAC model %s is not defined in the model block!', pacname)
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end
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% Get PAC model description
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pacmodel = DynareModel.pac.(pacname);
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pacmodel = M_.pac.(pacname);
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if pacmodel.model_consistent_expectations
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error('This function cannot be used with Model Consistent Expectation. Try pac.mce.parameters instead.')
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end
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% Get the name of the associated auxiliary model (VAR or TREND_COMPONENT) model and test its existence.
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if ~isfield(DynareModel.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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if ~isfield(M_.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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error('Unknown auxiliary model (%s) in PAC model (%s)!', pacmodel.auxiliary_model_name, pacname)
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end
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varmodel = DynareModel.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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varmodel = M_.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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% Check that we have the values of the VAR or TREND_COMPONENT matrices.
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if ~isfield(DynareOutput.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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if ~isfield(oo_.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
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error('Auxiliary model %s has to be estimated first!', pacmodel.auxiliary_model_name)
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end
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varcalib = DynareOutput.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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varcalib = oo_.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
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if ~isfield(varcalib, 'CompanionMatrix') || any(isnan(varcalib.CompanionMatrix(:)))
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error('Auxiliary model %s has to be estimated first.', pacmodel.auxiliary_model_name)
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end
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@ -79,13 +79,13 @@ else
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end
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% Build the vector of PAC parameters (ECM parameter + autoregressive parameters).
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pacvalues = DynareModel.params([pacmodel.ec.params; pacmodel.ar.params(1:pacmodel.max_lag)']);
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pacvalues = M_.params([pacmodel.ec.params; pacmodel.ar.params(1:pacmodel.max_lag)']);
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% Get the indices for the stationary/nonstationary variables in the VAR system.
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if numberofcomponents
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id = cell(numberofcomponents, 1);
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for i=1:numberofcomponents
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id(i) = {find(strcmp(DynareModel.endo_names{pacmodel.components(i).endo_var}, varmodel.list_of_variables_in_companion_var))};
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id(i) = {find(strcmp(M_.endo_names{pacmodel.components(i).endo_var}, varmodel.list_of_variables_in_companion_var))};
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if isempty(id{i})
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% Find the auxiliary variables if any
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ad = find(cell2mat(cellfun(@(x) isauxiliary(x, [8 10]), varmodel.list_of_variables_in_companion_var, 'UniformOutput', false)));
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@ -93,7 +93,7 @@ if numberofcomponents
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error('Cannot find the trend variable in the Companion VAR/VECM model.')
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else
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for j=1:length(ad)
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auxinfo = DynareModel.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(j)}));
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auxinfo = M_.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(j)}));
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if isequal(auxinfo.endo_index, pacmodel.components(i).endo_var)
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id(i) = {ad(j)};
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break
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end
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end
|
||||
else
|
||||
id = {find(strcmp(DynareModel.endo_names{pacmodel.ec.vars(pacmodel.ec.istarget)}, varmodel.list_of_variables_in_companion_var))};
|
||||
id = {find(strcmp(M_.endo_names{pacmodel.ec.vars(pacmodel.ec.istarget)}, varmodel.list_of_variables_in_companion_var))};
|
||||
if isempty(id{1})
|
||||
% Find the auxiliary variables if any
|
||||
ad = find(cell2mat(cellfun(@(x) isauxiliary(x, [8 10]), varmodel.list_of_variables_in_companion_var, 'UniformOutput', false)));
|
||||
|
@ -118,7 +118,7 @@ else
|
|||
error('Cannot find the trend variable in the Companion VAR/VECM model.')
|
||||
else
|
||||
for i=1:length(ad)
|
||||
auxinfo = DynareModel.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(i)}));
|
||||
auxinfo = M_.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(i)}));
|
||||
if isequal(auxinfo.endo_index, pacmodel.ec.vars(pacmodel.ec.istarget))
|
||||
id = {ad(i)};
|
||||
break
|
||||
|
@ -180,7 +180,7 @@ else
|
|||
end
|
||||
|
||||
% Get the value of the discount factor.
|
||||
beta = DynareModel.params(pacmodel.discount_index);
|
||||
beta = M_.params(pacmodel.discount_index);
|
||||
|
||||
% Is growth argument passed to pac_expectation?
|
||||
if isfield(pacmodel, 'growth_str')
|
||||
|
@ -197,7 +197,7 @@ end
|
|||
|
||||
% Do we have rule of thumb agents? γ is the share of optimizing agents.
|
||||
if isfield(pacmodel, 'non_optimizing_behaviour')
|
||||
gamma = DynareModel.params(pacmodel.share_of_optimizing_agents_index);
|
||||
gamma = M_.params(pacmodel.share_of_optimizing_agents_index);
|
||||
else
|
||||
gamma = 1.0;
|
||||
end
|
||||
|
@ -218,29 +218,29 @@ end
|
|||
|
||||
% Update M_.params with h
|
||||
if isequal(pacmodel.auxiliary_model_type, 'var')
|
||||
if DynareModel.var.(pacmodel.auxiliary_model_name).isconstant
|
||||
if M_.var.(pacmodel.auxiliary_model_name).isconstant
|
||||
if isfield(pacmodel, 'h_param_indices')
|
||||
% No decomposition
|
||||
DynareModel.params(pacmodel.h_param_indices) = h{1};
|
||||
M_.params(pacmodel.h_param_indices) = h{1};
|
||||
else
|
||||
for i=1:numberofcomponents
|
||||
DynareModel.params(pacmodel.components(i).h_param_indices) = h{i};
|
||||
M_.params(pacmodel.components(i).h_param_indices) = h{i};
|
||||
end
|
||||
end
|
||||
else
|
||||
if isfield(pacmodel, 'h_param_indices')
|
||||
% No decomposition
|
||||
DynareModel.params(pacmodel.h_param_indices(1)) = .0;
|
||||
DynareModel.params(pacmodel.h_param_indices(2:end)) = h{1};
|
||||
M_.params(pacmodel.h_param_indices(1)) = .0;
|
||||
M_.params(pacmodel.h_param_indices(2:end)) = h{1};
|
||||
else
|
||||
for i=1:numberofcomponents
|
||||
DynareModel.params(pacmodel.components(i).h_param_indices(1)) = .0;
|
||||
DynareModel.params(pacmodel.components(i).h_param_indices(2:end)) = h{i};
|
||||
M_.params(pacmodel.components(i).h_param_indices(1)) = .0;
|
||||
M_.params(pacmodel.components(i).h_param_indices(2:end)) = h{i};
|
||||
end
|
||||
end
|
||||
end % If the auxiliary model (VAR) has no constant.
|
||||
else
|
||||
DynareModel.params(pacmodel.h_param_indices) = h{1};
|
||||
M_.params(pacmodel.h_param_indices) = h{1};
|
||||
end % if auxiliary model is a VAR
|
||||
|
||||
% Update the parameter related to the growth neutrality correction.
|
||||
|
@ -263,7 +263,7 @@ if growth_flag
|
|||
if isnan(pacmodel.optim_additive.params(i)) && islogical(pacmodel.optim_additive.bgp{i}) && pacmodel.optim_additive.bgp{i}
|
||||
tmp0 = tmp0 + pacmodel.optim_additive.scaling_factor(i);
|
||||
elseif ~isnan(pacmodel.optim_additive.params(i)) && islogical(pacmodel.optim_additive.bgp{i}) && pacmodel.optim_additive.bgp{i}
|
||||
tmp0 = tmp0 + DynareModel.params(pacmodel.optim_additive.params(i))*pacmodel.optim_additive.scaling_factor(i);
|
||||
tmp0 = tmp0 + M_.params(pacmodel.optim_additive.params(i))*pacmodel.optim_additive.scaling_factor(i);
|
||||
elseif ~islogical(pacmodel.optim_additive.bgp{i})
|
||||
error('It is not possible to provide a value for the mean of an exogenous variable appearing in the optimal part of the PAC equation.')
|
||||
end
|
||||
|
@ -279,7 +279,7 @@ if growth_flag
|
|||
if isnan(pacmodel.non_optimizing_behaviour.params(i)) && islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && pacmodel.non_optimizing_behaviour.bgp{i}
|
||||
tmp0 = tmp0 + pacmodel.non_optimizing_behaviour.scaling_factor(i);
|
||||
elseif ~isnan(pacmodel.non_optimizing_behaviour.params(i)) && islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && pacmodel.non_optimizing_behaviour.bgp{i}
|
||||
tmp0 = tmp0 + DynareModel.params(pacmodel.non_optimizing_behaviour.params(i))*pacmodel.non_optimizing_behaviour.scaling_factor(i);
|
||||
tmp0 = tmp0 + M_.params(pacmodel.non_optimizing_behaviour.params(i))*pacmodel.non_optimizing_behaviour.scaling_factor(i);
|
||||
elseif ~islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && isnumeric(pacmodel.non_optimizing_behaviour.bgp{i}) && isnan(pacmodel.non_optimizing_behaviour.params(i))
|
||||
tmp1 = tmp1 + pacmodel.non_optimizing_behaviour.scaling_factor(i)*pacmodel.non_optimizing_behaviour.bgp{i};
|
||||
elseif ~islogical(pacmodel.non_optimizing_behaviour.bgp{i}) && isnumeric(pacmodel.non_optimizing_behaviour.bgp{i}) && ~isnan(pacmodel.non_optimizing_behaviour.params(i))
|
||||
|
@ -298,7 +298,7 @@ if growth_flag
|
|||
if isnan(pacmodel.additive.params(i)) && islogical(pacmodel.additive.bgp{i}) && pacmodel.additive.bgp{i}
|
||||
tmp0 = tmp0 + pacmodel.additive.scaling_factor(i);
|
||||
elseif ~isnan(pacmodel.additive.params(i)) && islogical(pacmodel.additive.bgp{i}) && pacmodel.additive.bgp{i}
|
||||
tmp0 = tmp0 + DynareModel.params(pacmodel.additive.params(i))*pacmodel.additive.scaling_factor(i);
|
||||
tmp0 = tmp0 + M_.params(pacmodel.additive.params(i))*pacmodel.additive.scaling_factor(i);
|
||||
elseif ~islogical(pacmodel.additive.bgp{i}) && isnumeric(pacmodel.additive.bgp{i}) && isnan(pacmodel.additive.params(i))
|
||||
tmp1 = tmp1 + pacmodel.additive.scaling_factor(i)*pacmodel.additive.bgp{i};
|
||||
elseif ~islogical(pacmodel.additive.bgp{i}) && isnumeric(pacmodel.additive.bgp{i}) && ~isnan(pacmodel.additive.params(i))
|
||||
|
@ -309,9 +309,9 @@ if growth_flag
|
|||
ll = ll - tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
|
||||
end
|
||||
if isfield(pacmodel, 'growth_neutrality_param_index')
|
||||
DynareModel.params(pacmodel.growth_neutrality_param_index) = cc/gamma; % Multiplies the variable or expression provided though the growth option in command pac_model.
|
||||
M_.params(pacmodel.growth_neutrality_param_index) = cc/gamma; % Multiplies the variable or expression provided though the growth option in command pac_model.
|
||||
else
|
||||
DynareModel.params(pacmodel.components(j).growth_neutrality_param_index) = cc/gamma; % Multiplies the variable or expression provided though the growth option in command pac_model.
|
||||
M_.params(pacmodel.components(j).growth_neutrality_param_index) = cc/gamma; % Multiplies the variable or expression provided though the growth option in command pac_model.
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
|
@ -1,19 +1,19 @@
|
|||
function DynareModel = update_parameters(varexpectationmodelname, DynareModel, DynareOutput)
|
||||
|
||||
function M_ = update_parameters(varexpectationmodelname, M_, oo_)
|
||||
% function M_ = update_parameters(varexpectationmodelname, M_, oo_)
|
||||
% Updates the VAR expectation reduced form parameters.
|
||||
%
|
||||
% INPUTS
|
||||
% - varexpectationmodelname [string] Name of the pac equation.
|
||||
% - DynareModel [struct] M_ global structure (model properties)
|
||||
% - DynareOutput [struct] oo_ global structure (model results)
|
||||
% - M_ [struct] global structure (model properties)
|
||||
% - oo_ [struct] oo_ global structure (model results)
|
||||
%
|
||||
% OUTPUTS
|
||||
% - DynareModel [struct] M_ global structure (with updated params field)
|
||||
% - M_ [struct] M_ global structure (with updated params field)
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2018-2021 Dynare Team
|
||||
% Copyright © 2018-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -36,26 +36,26 @@ if ~isrow(varexpectationmodelname)==1 || ~ischar(varexpectationmodelname)
|
|||
end
|
||||
|
||||
% Check that the model exists.
|
||||
if ~isfield(DynareModel.var_expectation, varexpectationmodelname)
|
||||
if ~isfield(M_.var_expectation, varexpectationmodelname)
|
||||
error('VAR_EXPECTATION_MODEL %s is not defined!', varexpectationmodelname)
|
||||
end
|
||||
|
||||
% Get the VAR model description
|
||||
varexpectationmodel = DynareModel.var_expectation.(varexpectationmodelname);
|
||||
varexpectationmodel = M_.var_expectation.(varexpectationmodelname);
|
||||
|
||||
% Get the name of the associated VAR model and test its existence.
|
||||
if ~isfield(DynareModel.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
|
||||
if ~isfield(M_.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
|
||||
error('Unknown VAR (%s) in VAR_EXPECTATION_MODEL (%s)!', varexpectationmodel.auxiliary_model_name, varexpectationmodelname)
|
||||
end
|
||||
|
||||
auxmodel = DynareModel.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.auxiliary_model_name);
|
||||
auxmodel = M_.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.auxiliary_model_name);
|
||||
|
||||
% Check that we have the values of the VAR matrices.
|
||||
if ~isfield(DynareOutput.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
|
||||
if ~isfield(oo_.(varexpectationmodel.auxiliary_model_type), varexpectationmodel.auxiliary_model_name)
|
||||
error('Auxiliary model %s has to be estimated or calibrated first!', varexpectationmodel.auxiliary_model_name)
|
||||
end
|
||||
|
||||
auxcalib = DynareOutput.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.auxiliary_model_name);
|
||||
auxcalib = oo_.(varexpectationmodel.auxiliary_model_type).(varexpectationmodel.auxiliary_model_name);
|
||||
|
||||
if ~isfield(auxcalib, 'CompanionMatrix') || any(isnan(auxcalib.CompanionMatrix(:)))
|
||||
message = sprintf('Auxiliary model %s has to be estimated first.', varexpectationmodel.auxiliary_model_name);
|
||||
|
@ -68,7 +68,7 @@ if isfield(varexpectationmodel, 'discount_value')
|
|||
discountfactor = varexpectationmodel.discount_value;
|
||||
else
|
||||
if isfield(varexpectationmodel, 'discount_index')
|
||||
discountfactor = DynareModel.params(varexpectationmodel.discount_index);
|
||||
discountfactor = M_.params(varexpectationmodel.discount_index);
|
||||
else
|
||||
error('This is most likely a bug. Pleasse conntact the Dynare Team.')
|
||||
end
|
||||
|
@ -100,11 +100,11 @@ end
|
|||
m = length(varexpectationmodel.expr.vars);
|
||||
variables_id_in_var = NaN(m,1);
|
||||
for i = 1:m
|
||||
j = find(strcmp(auxmodel.list_of_variables_in_companion_var, DynareModel.endo_names{varexpectationmodel.expr.vars(i)}));
|
||||
j = find(strcmp(auxmodel.list_of_variables_in_companion_var, M_.endo_names{varexpectationmodel.expr.vars(i)}));
|
||||
if isempty(j)
|
||||
error('Cannot find variable %s in the companion VAR', DynareModel.endo_names{varexpectationmodel.expr.vars(i)})
|
||||
error('Cannot find variable %s in the companion VAR', M_.endo_names{varexpectationmodel.expr.vars(i)})
|
||||
else
|
||||
variables_id_in_var(i) = find(strcmp(auxmodel.list_of_variables_in_companion_var, DynareModel.endo_names{varexpectationmodel.expr.vars(i)}));
|
||||
variables_id_in_var(i) = find(strcmp(auxmodel.list_of_variables_in_companion_var, M_.endo_names{varexpectationmodel.expr.vars(i)}));
|
||||
end
|
||||
end
|
||||
|
||||
|
@ -210,12 +210,12 @@ end
|
|||
|
||||
% Update reduced form parameters in M_.params.
|
||||
if isequal(varexpectationmodel.auxiliary_model_type, 'var')
|
||||
if DynareModel.var.(varexpectationmodel.auxiliary_model_name).isconstant
|
||||
DynareModel.params(varexpectationmodel.param_indices) = parameters;
|
||||
if M_.var.(varexpectationmodel.auxiliary_model_name).isconstant
|
||||
M_.params(varexpectationmodel.param_indices) = parameters;
|
||||
else
|
||||
DynareModel.params(varexpectationmodel.param_indices(1)) = .0;
|
||||
DynareModel.params(varexpectationmodel.param_indices(2:end)) = parameters;
|
||||
M_.params(varexpectationmodel.param_indices(1)) = .0;
|
||||
M_.params(varexpectationmodel.param_indices(2:end)) = parameters;
|
||||
end
|
||||
else
|
||||
DynareModel.params(varexpectationmodel.param_indices) = parameters;
|
||||
M_.params(varexpectationmodel.param_indices) = parameters;
|
||||
end
|
|
@ -25,11 +25,11 @@ classdef dprior
|
|||
|
||||
methods
|
||||
|
||||
function o = dprior(BayesInfo, PriorTrunc, Uniform)
|
||||
function o = dprior(bayestopt_, PriorTrunc, Uniform)
|
||||
% Class constructor.
|
||||
%
|
||||
% INPUTS
|
||||
% - BayesInfo [struct] Informations about the prior distribution, aka bayestopt_.
|
||||
% - bayestopt_ [struct] Informations about the prior distribution, aka bayestopt_.
|
||||
% - PriorTrunc [double] scalar, probability mass to be excluded, aka options_.prior_trunc
|
||||
% - Uniform [logical] scalar, produce uniform random deviates on the prior support.
|
||||
%
|
||||
|
@ -38,17 +38,17 @@ classdef dprior
|
|||
%
|
||||
% REQUIREMENTS
|
||||
% None.
|
||||
o.p6 = BayesInfo.p6;
|
||||
o.p7 = BayesInfo.p7;
|
||||
o.p3 = BayesInfo.p3;
|
||||
o.p4 = BayesInfo.p4;
|
||||
bounds = prior_bounds(BayesInfo, PriorTrunc);
|
||||
o.p6 = bayestopt_.p6;
|
||||
o.p7 = bayestopt_.p7;
|
||||
o.p3 = bayestopt_.p3;
|
||||
o.p4 = bayestopt_.p4;
|
||||
bounds = prior_bounds(bayestopt_, PriorTrunc);
|
||||
o.lb = bounds.lb;
|
||||
o.ub = bounds.ub;
|
||||
if nargin>2 && Uniform
|
||||
prior_shape = repmat(5, length(o.p6), 1);
|
||||
else
|
||||
prior_shape = BayesInfo.pshape;
|
||||
prior_shape = bayestopt_.pshape;
|
||||
end
|
||||
o.beta_index = find(prior_shape==1);
|
||||
if ~isempty(o.beta_index)
|
||||
|
@ -246,23 +246,23 @@ end % classdef --*-- Unit tests --*--
|
|||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ BayesInfo.pshape = p0;
|
||||
%$ BayesInfo.p1 = p1;
|
||||
%$ BayesInfo.p2 = p2;
|
||||
%$ BayesInfo.p3 = p3;
|
||||
%$ BayesInfo.p4 = p4;
|
||||
%$ BayesInfo.p5 = p5;
|
||||
%$ BayesInfo.p6 = p6;
|
||||
%$ BayesInfo.p7 = p7;
|
||||
%$ bayestopt_.pshape = p0;
|
||||
%$ bayestopt_.p1 = p1;
|
||||
%$ bayestopt_.p2 = p2;
|
||||
%$ bayestopt_.p3 = p3;
|
||||
%$ bayestopt_.p4 = p4;
|
||||
%$ bayestopt_.p5 = p5;
|
||||
%$ bayestopt_.p6 = p6;
|
||||
%$ bayestopt_.p7 = p7;
|
||||
%$
|
||||
%$ ndraws = 1e5;
|
||||
%$ m0 = BayesInfo.p1; %zeros(14,1);
|
||||
%$ v0 = diag(BayesInfo.p2.^2); %zeros(14);
|
||||
%$ m0 = bayestopt_.p1; %zeros(14,1);
|
||||
%$ v0 = diag(bayestopt_.p2.^2); %zeros(14);
|
||||
%$
|
||||
%$ % Call the tested routine
|
||||
%$ try
|
||||
%$ % Instantiate dprior object
|
||||
%$ o = dprior(BayesInfo, prior_trunc, false);
|
||||
%$ o = dprior(bayestopt_, prior_trunc, false);
|
||||
%$ % Do simulations in a loop and estimate recursively the mean and the variance.
|
||||
%$ for i = 1:ndraws
|
||||
%$ draw = o.draw();
|
||||
|
@ -277,8 +277,8 @@ end % classdef --*-- Unit tests --*--
|
|||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
%$ t(2) = all(abs(m0-bayestopt_.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v0-diag(bayestopt_.p2.^2))<5e-3));
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:1
|
||||
|
@ -345,21 +345,21 @@ end % classdef --*-- Unit tests --*--
|
|||
%$ end
|
||||
%$ end
|
||||
%$
|
||||
%$ BayesInfo.pshape = p0;
|
||||
%$ BayesInfo.p1 = p1;
|
||||
%$ BayesInfo.p2 = p2;
|
||||
%$ BayesInfo.p3 = p3;
|
||||
%$ BayesInfo.p4 = p4;
|
||||
%$ BayesInfo.p5 = p5;
|
||||
%$ BayesInfo.p6 = p6;
|
||||
%$ BayesInfo.p7 = p7;
|
||||
%$ bayestopt_.pshape = p0;
|
||||
%$ bayestopt_.p1 = p1;
|
||||
%$ bayestopt_.p2 = p2;
|
||||
%$ bayestopt_.p3 = p3;
|
||||
%$ bayestopt_.p4 = p4;
|
||||
%$ bayestopt_.p5 = p5;
|
||||
%$ bayestopt_.p6 = p6;
|
||||
%$ bayestopt_.p7 = p7;
|
||||
%$
|
||||
%$ ndraws = 1e5;
|
||||
%$
|
||||
%$ % Call the tested routine
|
||||
%$ try
|
||||
%$ % Instantiate dprior object.
|
||||
%$ o = dprior(BayesInfo, prior_trunc, false);
|
||||
%$ o = dprior(bayestopt_, prior_trunc, false);
|
||||
%$ X = o.draws(ndraws);
|
||||
%$ m = mean(X, 2);
|
||||
%$ v = var(X, 0, 2);
|
||||
|
@ -369,8 +369,8 @@ end % classdef --*-- Unit tests --*--
|
|||
%$ end
|
||||
%$
|
||||
%$ if t(1)
|
||||
%$ t(2) = all(abs(m-BayesInfo.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v-BayesInfo.p2.^2)<5e-3));
|
||||
%$ t(2) = all(abs(m-bayestopt_.p1)<3e-3);
|
||||
%$ t(3) = all(all(abs(v-bayestopt_.p2.^2)<5e-3));
|
||||
%$ end
|
||||
%$ T = all(t);
|
||||
%@eof:2
|
||||
|
|
|
@ -63,7 +63,7 @@ for i=1:M_.exo_nbr
|
|||
end
|
||||
|
||||
% Set up initial conditions
|
||||
[initialcondition, periods, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, dynamic_resid, dynamic_g1, y] = ...
|
||||
[initialcondition, periods, innovations, options_local, M_local, oo_local, endonames, exonames, dynamic_resid, dynamic_g1, y] = ...
|
||||
simul_backward_model_init(initialcondition, periods, options_, M_, oo_, zeros(periods, M_.exo_nbr));
|
||||
|
||||
% Get vector of indices for the selected endogenous variables.
|
||||
|
@ -90,9 +90,9 @@ end
|
|||
|
||||
% Compute forecast without shock
|
||||
if options_.linear
|
||||
[ysim__0, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__0, errorflag] = simul_backward_linear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
else
|
||||
[ysim__0, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__0, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
end
|
||||
|
||||
if errorflag
|
||||
|
@ -110,9 +110,9 @@ if withuncertainty
|
|||
for i=1:B
|
||||
innovations = transpose(sigma*randn(M_.exo_nbr, periods));
|
||||
if options_.linear
|
||||
[ysim__, xsim__, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__, xsim__, errorflag] = simul_backward_linear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
else
|
||||
[ysim__, xsim__, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__, xsim__, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
end
|
||||
if errorflag
|
||||
error('Simulation failed.')
|
||||
|
|
|
@ -139,7 +139,7 @@ if ~isempty(innovationbaseline)
|
|||
end
|
||||
|
||||
% Set up initial conditions
|
||||
[initialcondition, periods, Innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, dynamic_resid, dynamic_g1, y] = ...
|
||||
[initialcondition, periods, Innovations, options_local, M_local, oo_local, endonames, exonames, dynamic_resid, dynamic_g1, y] = ...
|
||||
simul_backward_model_init(initialcondition, periods, options_, M_, oo_, Innovations);
|
||||
|
||||
% Get the covariance matrix of the shocks.
|
||||
|
@ -161,9 +161,9 @@ irfs = struct();
|
|||
% Baseline paths (get transition paths induced by the initial condition and
|
||||
% baseline innovations).
|
||||
if options_.linear
|
||||
[ysim__0, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, Innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__0, errorflag] = simul_backward_linear_model_(initialcondition, periods, options_local, M_local, oo_local, Innovations, dynamic_resid, dynamic_g1);
|
||||
else
|
||||
[ysim__0, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, Innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__0, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, options_local, M_local, oo_local, Innovations, dynamic_resid, dynamic_g1);
|
||||
end
|
||||
|
||||
if errorflag
|
||||
|
@ -200,9 +200,9 @@ for i=1:length(listofshocks)
|
|||
innovations(1,:) = innovations(1,:) + transpose(C(:,j));
|
||||
end
|
||||
if options_.linear
|
||||
[ysim__1, errorflag] = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__1, errorflag] = simul_backward_linear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
else
|
||||
[ysim__1, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, dynamic_resid, dynamic_g1);
|
||||
[ysim__1, errorflag] = simul_backward_nonlinear_model_(initialcondition, periods, options_local, M_local, oo_local, innovations, dynamic_resid, dynamic_g1);
|
||||
end
|
||||
if errorflag
|
||||
warning('Simulation failed. Cannot compute IRF for %s.', listofshocks{i})
|
||||
|
@ -215,9 +215,9 @@ for i=1:length(listofshocks)
|
|||
endo_simul__1 = feval(transform, ysim__1);
|
||||
end
|
||||
% Instantiate a dseries object (with all the endogenous variables)
|
||||
alldeviations = dseries(transpose(endo_simul__1-endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), DynareModel.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
alldeviations = dseries(transpose(endo_simul__1-endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), M_local.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
if nargout>2
|
||||
allirfs = dseries(transpose(endo_simul__1), initialcondition.init, endonames(1:M_.orig_endo_nbr), DynareModel.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
allirfs = dseries(transpose(endo_simul__1), initialcondition.init, endonames(1:M_.orig_endo_nbr), M_local.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
end
|
||||
% Extract a sub-dseries object
|
||||
if deterministicshockflag
|
||||
|
@ -234,6 +234,6 @@ for i=1:length(listofshocks)
|
|||
end
|
||||
|
||||
if nargout>1
|
||||
baseline = dseries(transpose(endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), DynareModel.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
baseline = dseries(transpose(endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), M_local.endo_names_tex(1:M_.orig_endo_nbr));
|
||||
baseline = merge(baseline, innovationbaseline);
|
||||
end
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function [residuals, info] = calibrateresiduals(dbase, info, DynareModel)
|
||||
|
||||
function [residuals, info] = calibrateresiduals(dbase, info, M_)
|
||||
% [residuals, info] = calibrateresiduals(dbase, info, M_)
|
||||
% Compute residuals in a backward model. Residuals are unobserved exogenous
|
||||
% variables appearing additively in equations and without lags. An equation
|
||||
% cannot have more than one residual, and a residual cannot appear in more
|
||||
|
@ -8,7 +8,7 @@ function [residuals, info] = calibrateresiduals(dbase, info, DynareModel)
|
|||
% INPUTS
|
||||
% - dbase [dseries] Object containing all the endogenous and observed exogenous variables.
|
||||
% - info [struct] Informations about the residuals.
|
||||
% - DynareModel [struct] M_ as produced by the preprocessor.
|
||||
% - M_ [struct] M_ as produced by the preprocessor.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - residuals [dseries] Object containing the identified residuals.
|
||||
|
@ -38,13 +38,13 @@ function [residuals, info] = calibrateresiduals(dbase, info, DynareModel)
|
|||
displayresidualsequationmapping = false;
|
||||
|
||||
% Get function handle for the dynamic model
|
||||
dynamic_resid = str2func([DynareModel.fname,'.sparse.dynamic_resid']);
|
||||
dynamic_resid = str2func([M_.fname,'.sparse.dynamic_resid']);
|
||||
|
||||
% Get data for all the endogenous variables.
|
||||
ydata = dbase{info.endonames{:}}.data;
|
||||
|
||||
% Define function to retrieve an equation name
|
||||
eqname = @(z) DynareModel.equations_tags{cellfun(@(x) x==z, DynareModel.equations_tags(:,1)) & cellfun(@(x) isequal(x, 'name'), DynareModel.equations_tags(:,2)),3};
|
||||
eqname = @(z) M_.equations_tags{cellfun(@(x) x==z, M_.equations_tags(:,1)) & cellfun(@(x) isequal(x, 'name'), M_.equations_tags(:,2)),3};
|
||||
|
||||
% Get data for all the exogenous variables. Missing exogenous variables, to be solved for, have NaN values.
|
||||
exogenousvariablesindbase = intersect(info.exonames, dbase.name);
|
||||
|
@ -56,7 +56,7 @@ xdata = allexogenousvariables.data;
|
|||
% Evaluate the dynamic equation
|
||||
n = size(ydata, 2);
|
||||
y = [ydata(1,:)'; ydata(2,:)'; NaN(n, 1)];
|
||||
r = dynamic_resid(y, xdata(2,:), DynareModel.params, zeros(n, 1));
|
||||
r = dynamic_resid(y, xdata(2,:), M_.params, zeros(n, 1));
|
||||
|
||||
% Check that the number of equations evaluating to NaN matches the number of residuals
|
||||
idr = find(isnan(r));
|
||||
|
@ -102,7 +102,7 @@ for i = 1:residuals.vobs
|
|||
info.residualindex(i) = {strmatch(residualname, allexogenousvariables.name, 'exact')};
|
||||
tmpxdata = xdata;
|
||||
tmpxdata(2, info.residualindex{i}) = 0;
|
||||
r = dynamic_resid(y, tmpxdata(2,:), DynareModel.params, zeros(n, 1));
|
||||
r = dynamic_resid(y, tmpxdata(2,:), M_.params, zeros(n, 1));
|
||||
info.equations(i) = { idr(find(~isnan(r(idr))))};
|
||||
end
|
||||
|
||||
|
@ -130,7 +130,7 @@ xdata(:,cell2mat(info.residualindex)) = 0;
|
|||
rdata = NaN(residuals.nobs, residuals.vobs);
|
||||
for t=2:size(xdata, 1)
|
||||
y = [ydata(t-1,:)'; ydata(t,:)'; NaN(n, 1)];
|
||||
r = dynamic_resid(y, xdata(t,:), DynareModel.params, zeros(n, 1));
|
||||
r = dynamic_resid(y, xdata(t,:), M_.params, zeros(n, 1));
|
||||
rdata(t,:) = transpose(r(cell2mat(info.equations)));
|
||||
end
|
||||
residuals = dseries(rdata, dbase.init, info.residuals);
|
||||
|
|
|
@ -1,12 +1,12 @@
|
|||
function [dbase, info] = checkdatabase(dbase, DynareModel, inversionflag, simulationflag)
|
||||
|
||||
function [dbase, info] = checkdatabase(dbase, M_, inversionflag, simulationflag)
|
||||
% [dbase, info] = checkdatabase(dbase, M_, inversionflag, simulationflag)
|
||||
% Check that dbase contains all the endogenous variables of the model, and
|
||||
% reorder the endogenous variables as declared in the mod file. If Dynare
|
||||
% adds auxiliary variables, for lags greater than 1 on endogenous variables,
|
||||
% endogenous variables in difference (which may be lagged), or lags on the
|
||||
% exogenous variables, then thee routine complete the database.
|
||||
|
||||
% Copyright © 2018-2021 Dynare Team
|
||||
% Copyright © 2018-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -23,42 +23,42 @@ function [dbase, info] = checkdatabase(dbase, DynareModel, inversionflag, simula
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
% if DynareModel.maximum_endo_lead
|
||||
% error('The model (%s) is assumed to be backward!', DynareModel.fname)
|
||||
% if M_.maximum_endo_lead
|
||||
% error('The model (%s) is assumed to be backward!', M_.fname)
|
||||
% end
|
||||
|
||||
if nargin<3
|
||||
inversionflag = false;
|
||||
end
|
||||
|
||||
if exist(sprintf('+%s/dynamic_set_auxiliary_series.m', DynareModel.fname), 'file')
|
||||
dbase = feval(sprintf('%s.dynamic_set_auxiliary_series', DynareModel.fname), dbase, DynareModel.params);
|
||||
if exist(sprintf('+%s/dynamic_set_auxiliary_series.m', M_.fname), 'file')
|
||||
dbase = feval(sprintf('%s.dynamic_set_auxiliary_series', M_.fname), dbase, M_.params);
|
||||
end
|
||||
|
||||
listoflaggedexogenousvariables = {};
|
||||
if ~isempty(DynareModel.aux_vars)
|
||||
listoflaggedexogenousvariables = DynareModel.exo_names([DynareModel.aux_vars(find([DynareModel.aux_vars.type]==3)).orig_index]);
|
||||
if ~isempty(M_.aux_vars)
|
||||
listoflaggedexogenousvariables = M_.exo_names([M_.aux_vars(find([M_.aux_vars.type]==3)).orig_index]);
|
||||
end
|
||||
|
||||
listoflaggedendogenousvariables = {};
|
||||
laggedendogenousvariablesidx = find(DynareModel.lead_lag_incidence(1,1:DynareModel.orig_endo_nbr));
|
||||
laggedendogenousvariablesidx = find(M_.lead_lag_incidence(1,1:M_.orig_endo_nbr));
|
||||
if ~isempty(laggedendogenousvariablesidx)
|
||||
listoflaggedendogenousvariables = DynareModel.endo_names(laggedendogenousvariablesidx);
|
||||
listoflaggedendogenousvariables = M_.endo_names(laggedendogenousvariablesidx);
|
||||
end
|
||||
if ~isempty(DynareModel.aux_vars)
|
||||
laggedendogenousvariablesidx = find([DynareModel.aux_vars.type]==1);
|
||||
if ~isempty(M_.aux_vars)
|
||||
laggedendogenousvariablesidx = find([M_.aux_vars.type]==1);
|
||||
if ~isempty(laggedendogenousvariablesidx)
|
||||
listoflaggedendogenousvariables = union(listoflaggedendogenousvariables, DynareModel.endo_names([DynareModel.aux_vars(laggedendogenousvariablesidx).orig_index]));
|
||||
listoflaggedendogenousvariables = union(listoflaggedendogenousvariables, M_.endo_names([M_.aux_vars(laggedendogenousvariablesidx).orig_index]));
|
||||
end
|
||||
laggedendogenousvariablesidx = find([DynareModel.aux_vars.type]==8);
|
||||
laggedendogenousvariablesidx = find([M_.aux_vars.type]==8);
|
||||
if ~isempty(laggedendogenousvariablesidx)
|
||||
listoflaggedendogenousvariables = union(listoflaggedendogenousvariables, DynareModel.endo_names([DynareModel.aux_vars(laggedendogenousvariablesidx).orig_index]));
|
||||
listoflaggedendogenousvariables = union(listoflaggedendogenousvariables, M_.endo_names([M_.aux_vars(laggedendogenousvariablesidx).orig_index]));
|
||||
end
|
||||
end
|
||||
|
||||
info = struct;
|
||||
info.endonames = DynareModel.endo_names;
|
||||
info.exonames = DynareModel.exo_names;
|
||||
info.endonames = M_.endo_names;
|
||||
info.exonames = M_.exo_names;
|
||||
info.computeresiduals = false;
|
||||
|
||||
% Check that all the endogenous variables are defined in dbase.
|
||||
|
|
|
@ -1,11 +1,11 @@
|
|||
function [dbase, info] = checkdatabaseforinversion(dbase, DynareModel)
|
||||
|
||||
function [dbase, info] = checkdatabaseforinversion(dbase, M_)
|
||||
% [dbase, info] = checkdatabaseforinversion(dbase, M_)
|
||||
% Check that dbase contains all the endogenous variables of the model, and
|
||||
% reorder the endogenous variables as declared in the mod file. If Dynare
|
||||
% adds auxiliary variables, for lags greater than 1 on endogebnous variables
|
||||
% or lags on the exogenous variables.
|
||||
|
||||
% Copyright © 2017-2018 Dynare Team
|
||||
% Copyright © 2017-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -22,4 +22,4 @@ function [dbase, info] = checkdatabaseforinversion(dbase, DynareModel)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
[dbase, info] = checkdatabase(dbase, DynareModel, true, false);
|
||||
[dbase, info] = checkdatabase(dbase, M_, true, false);
|
|
@ -1,8 +1,8 @@
|
|||
function l = get_lags_on_endogenous_variables(DynareModel)
|
||||
|
||||
function l = get_lags_on_endogenous_variables(M_)
|
||||
% l = get_lags_on_endogenous_variables(M_)
|
||||
% Returns a vector with the max lag for each endogenous variable.
|
||||
|
||||
% Copyright © 2017 Dynare Team
|
||||
% Copyright © 2017-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -19,13 +19,13 @@ function l = get_lags_on_endogenous_variables(DynareModel)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
l = zeros(DynareModel.orig_endo_nbr, 1);
|
||||
l(find(DynareModel.lead_lag_incidence(1,1:DynareModel.orig_endo_nbr))) = -1;
|
||||
l = zeros(M_.orig_endo_nbr, 1);
|
||||
l(find(M_.lead_lag_incidence(1,1:M_.orig_endo_nbr))) = -1;
|
||||
|
||||
if ~isempty(DynareModel.aux_vars)
|
||||
aux_var_for_lagged_endogenous = find([DynareModel.aux_vars(:).type]==1);
|
||||
if ~isempty(M_.aux_vars)
|
||||
aux_var_for_lagged_endogenous = find([M_.aux_vars(:).type]==1);
|
||||
for i=1:length(aux_var_for_lagged_endogenous)
|
||||
l(DynareModel.aux_vars(aux_var_for_lagged_endogenous(i)).orig_index) = ...
|
||||
DynareModel.aux_vars(aux_var_for_lagged_endogenous(i)).orig_lead_lag;
|
||||
l(M_.aux_vars(aux_var_for_lagged_endogenous(i)).orig_index) = ...
|
||||
M_.aux_vars(aux_var_for_lagged_endogenous(i)).orig_lead_lag;
|
||||
end
|
||||
end
|
|
@ -1,8 +1,8 @@
|
|||
function l = get_lags_on_exogenous_variables(DynareModel)
|
||||
|
||||
function l = get_lags_on_exogenous_variables(M_)
|
||||
% l = get_lags_on_exogenous_variables(M_)
|
||||
% Returns a vector with the max lag for each exogenous variable.
|
||||
|
||||
% Copyright © 2017 Dynare Team
|
||||
% Copyright © 2017-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -19,12 +19,12 @@ function l = get_lags_on_exogenous_variables(DynareModel)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
l = zeros(DynareModel.exo_nbr, 1);
|
||||
l = zeros(M_.exo_nbr, 1);
|
||||
|
||||
if ~isempty(DynareModel.aux_vars)
|
||||
aux_var_for_lagged_exogenous = find([DynareModel.aux_vars(:).type]==3);
|
||||
if ~isempty(M_.aux_vars)
|
||||
aux_var_for_lagged_exogenous = find([M_.aux_vars(:).type]==3);
|
||||
for i=1:length(aux_var_for_lagged_exogenous)
|
||||
l(DynareModel.aux_vars(aux_var_for_lagged_exogenous(i)).orig_index) = ...
|
||||
DynareModel.aux_vars(aux_var_for_lagged_exogenous(i)).orig_lead_lag-1;
|
||||
l(M_.aux_vars(aux_var_for_lagged_exogenous(i)).orig_index) = ...
|
||||
M_.aux_vars(aux_var_for_lagged_exogenous(i)).orig_lead_lag-1;
|
||||
end
|
||||
end
|
|
@ -12,8 +12,8 @@ function [simulation, errorflag] = simul_backward_model(initialconditions, sampl
|
|||
% - errorflag [logical] scalar, equal to false iff the simulation did not fail.
|
||||
%
|
||||
% REMARKS
|
||||
% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
|
||||
% variables are saved in DynareOutput.endo_simul.
|
||||
% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
|
||||
% variables are saved in oo_.endo_simul.
|
||||
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
|
||||
% through the shocks block.
|
||||
% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function check_dsge_var_model(Model, EstimatedParameters, BayesInfo)
|
||||
function check_dsge_var_model(M_, estim_params_, bayestopt_)
|
||||
|
||||
% Check if the dsge model can be estimated with the DSGE-VAR approach.
|
||||
|
||||
|
@ -19,26 +19,26 @@ function check_dsge_var_model(Model, EstimatedParameters, BayesInfo)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
if EstimatedParameters.nvn
|
||||
if estim_params_.nvn
|
||||
error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
|
||||
end
|
||||
|
||||
if EstimatedParameters.ncn
|
||||
if estim_params_.ncn
|
||||
error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
|
||||
end
|
||||
|
||||
if any(vec(Model.H))
|
||||
if any(vec(M_.H))
|
||||
error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
|
||||
end
|
||||
|
||||
if EstimatedParameters.ncx
|
||||
if estim_params_.ncx
|
||||
error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
|
||||
end
|
||||
|
||||
if Model.exo_nbr>1 && any(vec(tril(Model.Sigma_e,-1)))
|
||||
if M_.exo_nbr>1 && any(vec(tril(M_.Sigma_e,-1)))
|
||||
error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
|
||||
end
|
||||
|
||||
if isequal(BayesInfo.with_trend,1)
|
||||
if isequal(bayestopt_.with_trend,1)
|
||||
error('Estimation::DsgeVarLikelihood: Linear trend is not yet implemented!')
|
||||
end
|
||||
|
|
|
@ -61,7 +61,7 @@ if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
|
|||
end
|
||||
|
||||
% Fill or update bayestopt_ structure
|
||||
[xparam1, estim_params_, BayesOptions, lb, ub, Model] = set_prior(estim_params_, M_, options_);
|
||||
[xparam1, estim_params_, BayesOptions, lb, ub, M_local] = set_prior(estim_params_, M_, options_);
|
||||
% Set restricted state space
|
||||
options_plot_priors_old=options_.plot_priors;
|
||||
options_.plot_priors=0;
|
||||
|
@ -77,27 +77,27 @@ if isempty(options_.qz_criterium)
|
|||
changed_qz_criterium_flag = 1;
|
||||
end
|
||||
|
||||
Model.dname = Model.fname;
|
||||
M_local.dname = M_local.fname;
|
||||
|
||||
% Temporarly set options_.order equal to one
|
||||
order = options_.order;
|
||||
options_.order = 1;
|
||||
|
||||
if ismember('plot', varargin)
|
||||
plot_priors(BayesOptions, Model, estim_params_, options_)
|
||||
plot_priors(BayesOptions, M_local, estim_params_, options_)
|
||||
donesomething = true;
|
||||
end
|
||||
|
||||
if ismember('table', varargin)
|
||||
print_table_prior(lb, ub, options_, Model, BayesOptions, estim_params_);
|
||||
print_table_prior(lb, ub, options_, M_local, BayesOptions, estim_params_);
|
||||
donesomething = true;
|
||||
end
|
||||
|
||||
if ismember('simulate', varargin) % Prior simulations (BK).
|
||||
if ismember('moments(distribution)', varargin)
|
||||
results = prior_sampler(1, Model, BayesOptions, options_, oo_, estim_params_);
|
||||
results = prior_sampler(1, M_local, BayesOptions, options_, oo_, estim_params_);
|
||||
else
|
||||
results = prior_sampler(0, Model, BayesOptions, options_, oo_, estim_params_);
|
||||
results = prior_sampler(0, M_local, BayesOptions, options_, oo_, estim_params_);
|
||||
end
|
||||
% Display prior mass info
|
||||
skipline(2)
|
||||
|
@ -119,7 +119,7 @@ if ismember('simulate', varargin) % Prior simulations (BK).
|
|||
end
|
||||
|
||||
if ismember('optimize', varargin) % Prior optimization.
|
||||
optimize_prior(options_, Model, oo_, BayesOptions, estim_params_);
|
||||
optimize_prior(options_, M_local, oo_, BayesOptions, estim_params_);
|
||||
donesomething = true;
|
||||
end
|
||||
|
||||
|
@ -130,16 +130,16 @@ if ismember('moments', varargin) % Prior simulations (2nd order moments).
|
|||
k = find(isnan(xparam1));
|
||||
xparam1(k) = BayesOptions.p1(k);
|
||||
% Update vector of parameters and covariance matrices
|
||||
Model = set_all_parameters(xparam1, estim_params_, Model);
|
||||
M_local = set_all_parameters(xparam1, estim_params_, M_local);
|
||||
% Check model.
|
||||
check_model(Model);
|
||||
check_model(M_local);
|
||||
% Compute state space representation of the model.
|
||||
oo__ = oo_;
|
||||
oo__.dr = set_state_space(oo__.dr, Model);
|
||||
oo__.dr = set_state_space(oo__.dr, M_local);
|
||||
% Solve model
|
||||
[T,R,~,info,oo__.dr, Model.params] = dynare_resolve(Model , options_ , oo__.dr, oo__.steady_state, oo__.exo_steady_state, oo__.exo_det_steady_state,'restrict');
|
||||
[T,R,~,info,oo__.dr, M_local.params] = dynare_resolve(M_local , options_ , oo__.dr, oo__.steady_state, oo__.exo_steady_state, oo__.exo_det_steady_state,'restrict');
|
||||
if ~info(1)
|
||||
info=endogenous_prior_restrictions(T,R,Model , options__ , oo__.dr,oo__.steady_state,oo__.exo_steady_state,oo__.exo_det_steady_state);
|
||||
info=endogenous_prior_restrictions(T,R,M_local , options__ , oo__.dr,oo__.steady_state,oo__.exo_steady_state,oo__.exo_det_steady_state);
|
||||
end
|
||||
if info
|
||||
skipline()
|
||||
|
@ -149,19 +149,19 @@ if ismember('moments', varargin) % Prior simulations (2nd order moments).
|
|||
return
|
||||
end
|
||||
% Compute and display second order moments
|
||||
oo__ = disp_th_moments(oo__.dr, [], Model, options__, oo__);
|
||||
oo__ = disp_th_moments(oo__.dr, [], M_local, options__, oo__);
|
||||
skipline(2)
|
||||
donesomething = true;
|
||||
end
|
||||
|
||||
if ismember('moments(distribution)', varargin) % Prior simulations (BK).
|
||||
if ~ismember('simulate', varargin)
|
||||
results = prior_sampler(1, Model, BayesOptions, options_, oo_, estim_params_);
|
||||
results = prior_sampler(1, M_local, BayesOptions, options_, oo_, estim_params_);
|
||||
end
|
||||
priorpath = [Model.dname filesep() 'prior' filesep() 'draws' filesep()];
|
||||
priorpath = [M_local.dname filesep() 'prior' filesep() 'draws' filesep()];
|
||||
list_of_files = dir([priorpath 'prior_draws*']);
|
||||
FirstOrderMoments = NaN(Model.orig_endo_nbr, options_.prior_mc);
|
||||
SecondOrderMoments = NaN(Model.orig_endo_nbr, Model.orig_endo_nbr, options_.prior_mc);
|
||||
FirstOrderMoments = NaN(M_local.orig_endo_nbr, options_.prior_mc);
|
||||
SecondOrderMoments = NaN(M_local.orig_endo_nbr, M_local.orig_endo_nbr, options_.prior_mc);
|
||||
iter = 1;
|
||||
noprint = options_.noprint;
|
||||
options_.noprint = 1;
|
||||
|
@ -172,8 +172,8 @@ if ismember('moments(distribution)', varargin) % Prior simulations (BK).
|
|||
dr = tmp.pdraws{j,3};
|
||||
oo__ = oo_;
|
||||
oo__.dr = dr;
|
||||
Model=set_parameters_locally(Model,tmp.pdraws{j,1});% Needed to update the covariance matrix of the state innovations.
|
||||
oo__ = disp_th_moments(oo__.dr, [], Model, options_, oo__);
|
||||
M_local=set_parameters_locally(M_local,tmp.pdraws{j,1});% Needed to update the covariance matrix of the state innovations.
|
||||
oo__ = disp_th_moments(oo__.dr, [], M_local, options_, oo__);
|
||||
FirstOrderMoments(:,iter) = oo__.mean;
|
||||
SecondOrderMoments(:,:,iter) = oo__.var;
|
||||
iter = iter+1;
|
||||
|
|
|
@ -1,11 +1,11 @@
|
|||
function disp_steady_state(M,oo,options)
|
||||
% function disp_steady_state(M,oo,options)
|
||||
function disp_steady_state(M_,oo_,options_)
|
||||
% function disp_steady_state(M_,oo_,options_)
|
||||
% computes and prints the steady state calculations
|
||||
%
|
||||
% INPUTS
|
||||
% M structure of parameters
|
||||
% oo structure of results
|
||||
% options structure of options
|
||||
% M_ structure of parameters
|
||||
% oo_ structure of results
|
||||
% options_ structure of options
|
||||
%
|
||||
% OUTPUTS
|
||||
% none
|
||||
|
@ -13,7 +13,7 @@ function disp_steady_state(M,oo,options)
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2001-2020 Dynare Team
|
||||
% Copyright © 2001-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -31,15 +31,15 @@ function disp_steady_state(M,oo,options)
|
|||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
skipline()
|
||||
if options.loglinear
|
||||
if options_.loglinear
|
||||
disp('STEADY-STATE RESULTS FOR THE UNLOGGED VARIABLES:')
|
||||
else
|
||||
disp('STEADY-STATE RESULTS:')
|
||||
end
|
||||
skipline()
|
||||
endo_names = char(M.endo_names);
|
||||
steady_state = oo.steady_state;
|
||||
endo_names = char(M_.endo_names);
|
||||
steady_state = oo_.steady_state;
|
||||
|
||||
for i = 1:M.orig_endo_nbr
|
||||
for i = 1:M_.orig_endo_nbr
|
||||
fprintf('%s \t\t %g\n', endo_names(i,:), steady_state(i));
|
||||
end
|
||||
|
|
|
@ -1,15 +1,15 @@
|
|||
function forecast = dyn_forecast(var_list,M,options,oo,task,dataset_info)
|
||||
% function forecast = dyn_forecast(var_list,M,options,oo,task,dataset_info)
|
||||
function forecast = dyn_forecast(var_list,M_,options_,oo_,task,dataset_info)
|
||||
% function forecast = dyn_forecast(var_list,M_,options_,oo_,task,dataset_info)
|
||||
% computes mean forecast for a given value of the parameters
|
||||
% computes also confidence bands for the forecast
|
||||
%
|
||||
% INPUTS
|
||||
% var_list: list of variables (character matrix)
|
||||
% M: Dynare model structure
|
||||
% options: Dynare options structure
|
||||
% oo: Dynare results structure
|
||||
% task: indicates how to initialize the forecast
|
||||
% either 'simul' or 'smoother'
|
||||
% var_list: list of variables (character matrix)
|
||||
% M_: Dynare model structure
|
||||
% options_: Dynare options structure
|
||||
% oo_: Dynare results structure
|
||||
% task: indicates how to initialize the forecast
|
||||
% either 'simul' or 'smoother'
|
||||
% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
|
||||
|
||||
% OUTPUTS
|
||||
|
@ -27,7 +27,7 @@ function forecast = dyn_forecast(var_list,M,options,oo,task,dataset_info)
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2003-2022 Dynare Team
|
||||
% Copyright © 2003-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -44,23 +44,23 @@ function forecast = dyn_forecast(var_list,M,options,oo,task,dataset_info)
|
|||
% 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') || isempty(oo.dr)
|
||||
if ~isfield(oo_,'dr') || isempty(oo_.dr)
|
||||
error('dyn_forecast: the decision rules have not been computed. Did you forget a stoch_simul-command?')
|
||||
end
|
||||
|
||||
if nargin<6 && options.prefilter
|
||||
if nargin<6 && options_.prefilter
|
||||
error('The prefiltering option is not allowed without providing a dataset')
|
||||
elseif nargin==6
|
||||
mean_varobs=dataset_info.descriptive.mean';
|
||||
end
|
||||
|
||||
oo=make_ex_(M,options,oo);
|
||||
oo_=make_ex_(M_,options_,oo_);
|
||||
|
||||
maximum_lag = M.maximum_lag;
|
||||
maximum_lag = M_.maximum_lag;
|
||||
|
||||
endo_names = M.endo_names;
|
||||
endo_names = M_.endo_names;
|
||||
if isempty(var_list)
|
||||
var_list = endo_names(1:M.orig_endo_nbr);
|
||||
var_list = endo_names(1:M_.orig_endo_nbr);
|
||||
end
|
||||
i_var = [];
|
||||
for i = 1:length(var_list)
|
||||
|
@ -76,108 +76,108 @@ n_var = length(i_var);
|
|||
trend = 0;
|
||||
switch task
|
||||
case 'simul'
|
||||
horizon = options.periods;
|
||||
horizon = options_.periods;
|
||||
if horizon == 0
|
||||
horizon = 5;
|
||||
end
|
||||
if isempty(M.endo_histval)
|
||||
if options.loglinear && ~options.logged_steady_state
|
||||
y0 = repmat(log(oo.dr.ys),1,maximum_lag);
|
||||
if isempty(M_.endo_histval)
|
||||
if options_.loglinear && ~options_.logged_steady_state
|
||||
y0 = repmat(log(oo_.dr.ys),1,maximum_lag);
|
||||
else
|
||||
y0 = repmat(oo.dr.ys,1,maximum_lag);
|
||||
y0 = repmat(oo_.dr.ys,1,maximum_lag);
|
||||
end
|
||||
else
|
||||
if options.loglinear
|
||||
y0 = log_variable(1:M.endo_nbr,M.endo_histval,M);
|
||||
if options_.loglinear
|
||||
y0 = log_variable(1:M_.endo_nbr,M_.endo_histval,M_);
|
||||
else
|
||||
y0 = M.endo_histval;
|
||||
y0 = M_.endo_histval;
|
||||
end
|
||||
end
|
||||
case 'smoother'
|
||||
horizon = options.forecast;
|
||||
if isnan(options.first_obs)
|
||||
horizon = options_.forecast;
|
||||
if isnan(options_.first_obs)
|
||||
first_obs=1;
|
||||
else
|
||||
first_obs=options.first_obs;
|
||||
first_obs=options_.first_obs;
|
||||
end
|
||||
if isfield(oo.SmoothedVariables,'Mean')
|
||||
y_smoothed = oo.SmoothedVariables.Mean;
|
||||
if isfield(oo_.SmoothedVariables,'Mean')
|
||||
y_smoothed = oo_.SmoothedVariables.Mean;
|
||||
else
|
||||
y_smoothed = oo.SmoothedVariables;
|
||||
y_smoothed = oo_.SmoothedVariables;
|
||||
end
|
||||
y0 = zeros(M.endo_nbr,maximum_lag);
|
||||
for i = 1:M.endo_nbr
|
||||
v_name = M.endo_names{i};
|
||||
y0 = zeros(M_.endo_nbr,maximum_lag);
|
||||
for i = 1:M_.endo_nbr
|
||||
v_name = M_.endo_names{i};
|
||||
y0(i,:) = y_smoothed.(v_name)(end-maximum_lag+1:end); %includes steady state or mean, but simult_ will subtract only steady state
|
||||
% 2. Subtract mean/steady state and add steady state; takes care of prefiltering
|
||||
if isfield(oo.Smoother,'Constant') && isfield(oo.Smoother.Constant,v_name)
|
||||
y0(i,:)=y0(i,:)-oo.Smoother.Constant.(v_name)(end-maximum_lag+1:end); %subtract mean or steady state
|
||||
if options.loglinear
|
||||
y0(i,:)=y0(i,:)+log_variable(i,oo.dr.ys,M);
|
||||
if isfield(oo_.Smoother,'Constant') && isfield(oo_.Smoother.Constant,v_name)
|
||||
y0(i,:)=y0(i,:)-oo_.Smoother.Constant.(v_name)(end-maximum_lag+1:end); %subtract mean or steady state
|
||||
if options_.loglinear
|
||||
y0(i,:)=y0(i,:)+log_variable(i,oo_.dr.ys,M_);
|
||||
else
|
||||
y0(i,:)=y0(i,:)+oo.dr.ys(strmatch(v_name, M.endo_names, 'exact'));
|
||||
y0(i,:)=y0(i,:)+oo_.dr.ys(strmatch(v_name, M_.endo_names, 'exact'));
|
||||
end
|
||||
end
|
||||
% 2. Subtract trend
|
||||
if isfield(oo.Smoother,'Trend') && isfield(oo.Smoother.Trend,v_name)
|
||||
y0(i,:)=y0(i,:)-oo.Smoother.Trend.(v_name)(end-maximum_lag+1:end); %subtract trend, which is not subtracted by simult_
|
||||
if isfield(oo_.Smoother,'Trend') && isfield(oo_.Smoother.Trend,v_name)
|
||||
y0(i,:)=y0(i,:)-oo_.Smoother.Trend.(v_name)(end-maximum_lag+1:end); %subtract trend, which is not subtracted by simult_
|
||||
end
|
||||
end
|
||||
gend = options.nobs;
|
||||
if isfield(oo.Smoother,'TrendCoeffs')
|
||||
var_obs = options.varobs;
|
||||
endo_names = M.endo_names;
|
||||
gend = options_.nobs;
|
||||
if isfield(oo_.Smoother,'TrendCoeffs')
|
||||
var_obs = options_.varobs;
|
||||
endo_names = M_.endo_names;
|
||||
i_var_obs = [];
|
||||
trend_coeffs = [];
|
||||
for i=1:length(var_obs)
|
||||
tmp = strmatch(var_obs{i}, endo_names(i_var), 'exact');
|
||||
trend_var_index = strmatch(var_obs{i}, M.endo_names, 'exact');
|
||||
trend_var_index = strmatch(var_obs{i}, M_.endo_names, 'exact');
|
||||
if ~isempty(tmp)
|
||||
i_var_obs = [ i_var_obs; tmp];
|
||||
trend_coeffs = [trend_coeffs; oo.Smoother.TrendCoeffs(trend_var_index)];
|
||||
trend_coeffs = [trend_coeffs; oo_.Smoother.TrendCoeffs(trend_var_index)];
|
||||
end
|
||||
end
|
||||
if ~isempty(trend_coeffs)
|
||||
trend = trend_coeffs*(first_obs+gend-1+(1-M.maximum_lag:horizon));
|
||||
if options.prefilter
|
||||
trend = trend_coeffs*(first_obs+gend-1+(1-M_.maximum_lag:horizon));
|
||||
if options_.prefilter
|
||||
trend = trend - repmat(mean(trend_coeffs*[first_obs:first_obs+gend-1],2),1,horizon+1); %subtract mean trend
|
||||
end
|
||||
end
|
||||
else
|
||||
trend_coeffs=zeros(length(options.varobs),1);
|
||||
trend_coeffs=zeros(length(options_.varobs),1);
|
||||
end
|
||||
otherwise
|
||||
error('Wrong flag value')
|
||||
end
|
||||
|
||||
if M.exo_det_nbr == 0
|
||||
if isequal(M.H,0)
|
||||
[yf,int_width] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
|
||||
if M_.exo_det_nbr == 0
|
||||
if isequal(M_.H,0)
|
||||
[yf,int_width] = forcst(oo_.dr,y0,horizon,var_list,M_,oo_,options_);
|
||||
else
|
||||
[yf,int_width,int_width_ME] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
|
||||
[yf,int_width,int_width_ME] = forcst(oo_.dr,y0,horizon,var_list,M_,oo_,options_);
|
||||
end
|
||||
else
|
||||
exo_det_length = size(oo.exo_det_simul,1)-M.maximum_lag;
|
||||
exo_det_length = size(oo_.exo_det_simul,1)-M_.maximum_lag;
|
||||
if horizon > exo_det_length
|
||||
ex = zeros(horizon,M.exo_nbr);
|
||||
oo.exo_det_simul = [ oo.exo_det_simul;...
|
||||
repmat(oo.exo_det_steady_state',...
|
||||
ex = zeros(horizon,M_.exo_nbr);
|
||||
oo_.exo_det_simul = [ oo_.exo_det_simul;...
|
||||
repmat(oo_.exo_det_steady_state',...
|
||||
horizon- ...
|
||||
exo_det_length,1)];
|
||||
elseif horizon <= exo_det_length
|
||||
ex = zeros(exo_det_length,M.exo_nbr);
|
||||
ex = zeros(exo_det_length,M_.exo_nbr);
|
||||
end
|
||||
if options.linear
|
||||
if options_.linear
|
||||
iorder = 1;
|
||||
else
|
||||
iorder = options.order;
|
||||
iorder = options_.order;
|
||||
end
|
||||
if isequal(M.H,0)
|
||||
[yf,int_width] = simultxdet(y0,ex,oo.exo_det_simul,...
|
||||
iorder,var_list,M,oo,options);
|
||||
if isequal(M_.H,0)
|
||||
[yf,int_width] = simultxdet(y0,ex,oo_.exo_det_simul,...
|
||||
iorder,var_list,M_,oo_,options_);
|
||||
else
|
||||
[yf,int_width,int_width_ME] = simultxdet(y0,ex,oo.exo_det_simul,...
|
||||
iorder,var_list,M,oo,options);
|
||||
[yf,int_width,int_width_ME] = simultxdet(y0,ex,oo_.exo_det_simul,...
|
||||
iorder,var_list,M_,oo_,options_);
|
||||
end
|
||||
end
|
||||
|
||||
|
@ -185,13 +185,13 @@ if ~isscalar(trend) %add trend back to forecast
|
|||
yf(i_var_obs,:) = yf(i_var_obs,:) + trend;
|
||||
end
|
||||
|
||||
if options.loglinear
|
||||
if options.prefilter == 1 %subtract steady state and add mean for observables
|
||||
yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(log(oo.dr.ys(i_var_obs)),1,horizon+M.maximum_lag)+ repmat(mean_varobs,1,horizon+M.maximum_lag);
|
||||
if options_.loglinear
|
||||
if options_.prefilter == 1 %subtract steady state and add mean for observables
|
||||
yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(log(oo_.dr.ys(i_var_obs)),1,horizon+M_.maximum_lag)+ repmat(mean_varobs,1,horizon+M_.maximum_lag);
|
||||
end
|
||||
else
|
||||
if options.prefilter == 1 %subtract steady state and add mean for observables
|
||||
yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(oo.dr.ys(i_var_obs),1,horizon+M.maximum_lag)+ repmat(mean_varobs,1,horizon+M.maximum_lag);
|
||||
if options_.prefilter == 1 %subtract steady state and add mean for observables
|
||||
yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(oo_.dr.ys(i_var_obs),1,horizon+M_.maximum_lag)+ repmat(mean_varobs,1,horizon+M_.maximum_lag);
|
||||
end
|
||||
end
|
||||
|
||||
|
@ -200,17 +200,17 @@ for i=1:n_var
|
|||
forecast.Mean.(vname) = yf(i,maximum_lag+(1:horizon))';
|
||||
forecast.HPDinf.(vname)= yf(i,maximum_lag+(1:horizon))' - int_width(1:horizon,i);
|
||||
forecast.HPDsup.(vname) = yf(i,maximum_lag+(1:horizon))' + int_width(1:horizon,i);
|
||||
if ~isequal(M.H,0) && ismember(var_list{i},options.varobs)
|
||||
if ~isequal(M_.H,0) && ismember(var_list{i},options_.varobs)
|
||||
forecast.HPDinf_ME.(vname)= yf(i,maximum_lag+(1:horizon))' - int_width_ME(1:horizon,i);
|
||||
forecast.HPDsup_ME.(vname) = yf(i,maximum_lag+(1:horizon))' + int_width_ME(1:horizon,i);
|
||||
end
|
||||
end
|
||||
|
||||
for i=1:M.exo_det_nbr
|
||||
forecast.Exogenous.(M.exo_det_names{i}) = oo.exo_det_simul(maximum_lag+(1:horizon),i);
|
||||
for i=1:M_.exo_det_nbr
|
||||
forecast.Exogenous.(M_.exo_det_names{i}) = oo_.exo_det_simul(maximum_lag+(1:horizon),i);
|
||||
end
|
||||
|
||||
if ~options.nograph
|
||||
oo.forecast = forecast;
|
||||
forecast_graphs(var_list, M, oo, options)
|
||||
if ~options_.nograph
|
||||
oo_.forecast = forecast;
|
||||
forecast_graphs(var_list, M_, oo_, options_)
|
||||
end
|
|
@ -1,11 +1,11 @@
|
|||
function [lnpriormom] = endogenous_prior(data,dataset_info, Pstar,BayesInfo,H)
|
||||
function [lnpriormom] = endogenous_prior(data,dataset_info, Pstar,bayestopt_,H)
|
||||
% Computes the endogenous log prior addition to the initial prior
|
||||
%
|
||||
% INPUTS
|
||||
% data [double] n*T vector of data observations
|
||||
% dataset_info [structure] various information about the dataset
|
||||
% Pstar [double] k*k matrix of
|
||||
% BayesInfo [structure]
|
||||
% bayestopt_ [structure]
|
||||
%
|
||||
% OUTPUTS
|
||||
% lnpriormom [double] scalar of log endogenous prior value
|
||||
|
@ -80,7 +80,7 @@ Shat=C0 +(1-1/(2+1))*(C1+C1')...
|
|||
+(1-2/(2+1))*(C2+C2');
|
||||
|
||||
% Model variances below:
|
||||
mf=BayesInfo.mf1;
|
||||
mf=bayestopt_.mf1;
|
||||
II=eye(size(Pstar,2));
|
||||
Z=II(mf,:);
|
||||
% This is Ftheta, variance of model variables, given param vector theta:
|
||||
|
|
|
@ -1,15 +1,33 @@
|
|||
function e = ep_accuracy_check(M,options,oo)
|
||||
function e = ep_accuracy_check(M_,options_,oo_)
|
||||
% e = ep_accuracy_check(M_,options_,oo_)
|
||||
|
||||
endo_simul = oo.endo_simul;
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
endo_simul = oo_.endo_simul;
|
||||
n = size(endo_simul,2);
|
||||
[initialconditions, innovations, pfm, ep, verbosity, options, oo] = ...
|
||||
extended_path_initialization([], n-1, [], options, M, oo);
|
||||
[initialconditions, innovations, pfm, ep, verbosity, options_, oo_] = ...
|
||||
extended_path_initialization([], n-1, [], options_, M_, oo_);
|
||||
|
||||
options.ep.accuracy.stochastic.order = options.ep.stochastic.order;
|
||||
[nodes,weights,nnodes] = setup_integration_nodes(options.ep.accuracy,pfm);
|
||||
options_.ep.accuracy.stochastic.order = options_.ep.stochastic.order;
|
||||
[nodes,weights,nnodes] = setup_integration_nodes(options_.ep.accuracy,pfm);
|
||||
|
||||
e = zeros(M.endo_nbr,n);
|
||||
e = zeros(M_.endo_nbr,n);
|
||||
for i=1:n
|
||||
e(:,i) = euler_equation_error(endo_simul(:,i),oo.exo_simul, ...
|
||||
innovations, M, options,oo,pfm,nodes,weights);
|
||||
e(:,i) = euler_equation_error(endo_simul(:,i),oo_.exo_simul, ...
|
||||
innovations, M_, options_,oo_,pfm,nodes,weights);
|
||||
end
|
|
@ -55,9 +55,9 @@ solve_algo:
|
|||
stack_solve_algo:
|
||||
ut: (unscented free parameter)
|
||||
|
||||
pfm.stochastic_order = DynareOptions.ep.stochastic.order;
|
||||
pfm.periods = DynareOptions.ep.periods;
|
||||
pfm.verbose = DynareOptions.ep.verbosity;
|
||||
pfm.stochastic_order = options_.ep.stochastic.order;
|
||||
pfm.periods = options_.ep.periods;
|
||||
pfm.verbose = options_.ep.verbosity;
|
||||
|
||||
|
||||
* in extended_path_core, one passes options.ep and individual options
|
||||
|
|
|
@ -1,6 +1,7 @@
|
|||
function e = euler_equation_error(y0,x,innovations,M,options,oo,pfm,nodes,weights)
|
||||
function e = euler_equation_error(y0,x,innovations,M_,options_,oo_,pfm,nodes,weights)
|
||||
% e = euler_equation_error(y0,x,innovations,M_,options_,oo_,pfm,nodes,weights)
|
||||
|
||||
% Copyright © 2016-2020 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -17,34 +18,34 @@ function e = euler_equation_error(y0,x,innovations,M,options,oo,pfm,nodes,weight
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
dynamic_model = str2func([M.fname '.dynamic']);
|
||||
ep = options.ep;
|
||||
dynamic_model = str2func([M_.fname '.dynamic']);
|
||||
ep = options_.ep;
|
||||
[y1, info_convergence, endogenousvariablespaths] = extended_path_core(ep.periods, ...
|
||||
M.endo_nbr, M.exo_nbr, ...
|
||||
M_.endo_nbr, M_.exo_nbr, ...
|
||||
innovations.positive_var_indx, ...
|
||||
x, ep.init, y0, oo.steady_state, ...
|
||||
x, ep.init, y0, oo_.steady_state, ...
|
||||
0, ...
|
||||
ep.stochastic.order, M, ...
|
||||
ep.stochastic.order, M_, ...
|
||||
pfm, ep.stochastic.algo, ...
|
||||
ep.solve_algo, ...
|
||||
ep.stack_solve_algo, ...
|
||||
options.lmmcp, options, oo, ...
|
||||
options_.lmmcp, options_, oo_, ...
|
||||
[]);
|
||||
i_pred = find(M.lead_lag_incidence(1,:));
|
||||
i_fwrd = find(M.lead_lag_incidence(3,:));
|
||||
x1 = [x(2:end,:); zeros(1,M.exo_nbr)];
|
||||
i_pred = find(M_.lead_lag_incidence(1,:));
|
||||
i_fwrd = find(M_.lead_lag_incidence(3,:));
|
||||
x1 = [x(2:end,:); zeros(1,M_.exo_nbr)];
|
||||
for i=1:length(nodes)
|
||||
x2 = x1;
|
||||
x2(2,:) = x2(2,:) + nodes(i,:);
|
||||
[y2, info_convergence, endogenousvariablespaths] = ...
|
||||
extended_path_core(ep.periods, M.endo_nbr, M.exo_nbr, ...
|
||||
extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, ...
|
||||
innovations.positive_var_indx, x2, ep.init, ...
|
||||
y1, oo.steady_state, 0, ...
|
||||
ep.stochastic.order, M, pfm, ep.stochastic.algo, ...
|
||||
ep.solve_algo, ep.stack_solve_algo, options.lmmcp, ...
|
||||
options, oo, []);
|
||||
y1, oo_.steady_state, 0, ...
|
||||
ep.stochastic.order, M_, pfm, ep.stochastic.algo, ...
|
||||
ep.solve_algo, ep.stack_solve_algo, options_.lmmcp, ...
|
||||
options_, oo_, []);
|
||||
|
||||
z = [y0(i_pred); y1; y2(i_fwrd)];
|
||||
res(:,i) = dynamic_model(z,x,M.params,oo.steady_state,2);
|
||||
res(:,i) = dynamic_model(z,x,M_.params,oo_.steady_state,2);
|
||||
end
|
||||
e = res*weights;
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function [ts, DynareResults] = extended_path(initialconditions, samplesize, exogenousvariables, DynareOptions, DynareModel, DynareResults)
|
||||
|
||||
function [ts, oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
|
||||
% [ts, oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
|
||||
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
|
||||
% series of size T is obtained by solving T perfect foresight models.
|
||||
%
|
||||
|
@ -7,9 +7,9 @@ function [ts, DynareResults] = extended_path(initialconditions, samplesize, exog
|
|||
% o initialconditions [double] m*1 array, where m is the number of endogenous variables in the model.
|
||||
% o samplesize [integer] scalar, size of the sample to be simulated.
|
||||
% o exogenousvariables [double] T*n array, values for the structural innovations.
|
||||
% o DynareOptions [struct] options_
|
||||
% o DynareModel [struct] M_
|
||||
% o DynareResults [struct] oo_
|
||||
% o options_ [struct] options_
|
||||
% o M_ [struct] Dynare's model structure
|
||||
% o oo_ [struct] Dynare's results structure
|
||||
%
|
||||
% OUTPUTS
|
||||
% o ts [dseries] m*samplesize array, the simulations.
|
||||
|
@ -36,13 +36,13 @@ function [ts, DynareResults] = extended_path(initialconditions, samplesize, exog
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
[initialconditions, innovations, pfm, ep, verbosity, DynareOptions, DynareResults] = ...
|
||||
extended_path_initialization(initialconditions, samplesize, exogenousvariables, DynareOptions, DynareModel, DynareResults);
|
||||
[initialconditions, innovations, pfm, ep, verbosity, options_, oo_] = ...
|
||||
extended_path_initialization(initialconditions, samplesize, exogenousvariables, options_, M_, oo_);
|
||||
|
||||
[shocks, spfm_exo_simul, innovations, DynareResults] = extended_path_shocks(innovations, ep, exogenousvariables, samplesize,DynareModel,DynareOptions,DynareResults);
|
||||
[shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables, samplesize,M_,options_,oo_);
|
||||
|
||||
% Initialize the matrix for the paths of the endogenous variables.
|
||||
endogenous_variables_paths = NaN(DynareModel.endo_nbr,samplesize+1);
|
||||
endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
|
||||
endogenous_variables_paths(:,1) = initialconditions;
|
||||
|
||||
% Set waitbar (graphic or text mode)
|
||||
|
@ -63,18 +63,18 @@ while (t <= samplesize)
|
|||
if t>2
|
||||
% Set initial guess for the solver (using the solution of the
|
||||
% previous period problem).
|
||||
initialguess = [endogenousvariablespaths(:, 2:end), DynareResults.steady_state];
|
||||
initialguess = [endogenousvariablespaths(:, 2:end), oo_.steady_state];
|
||||
else
|
||||
initialguess = [];
|
||||
end
|
||||
[endogenous_variables_paths(:,t), info_convergence, endogenousvariablespaths] = extended_path_core(ep.periods, DynareModel.endo_nbr, DynareModel.exo_nbr, innovations.positive_var_indx, ...
|
||||
[endogenous_variables_paths(:,t), info_convergence, endogenousvariablespaths] = extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, innovations.positive_var_indx, ...
|
||||
spfm_exo_simul, ep.init, endogenous_variables_paths(:,t-1), ...
|
||||
DynareResults.steady_state, ...
|
||||
oo_.steady_state, ...
|
||||
verbosity, ep.stochastic.order, ...
|
||||
DynareModel, pfm, ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
DynareOptions.lmmcp, ...
|
||||
DynareOptions, ...
|
||||
DynareResults, initialguess);
|
||||
M_, pfm, ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
options_.lmmcp, ...
|
||||
options_, ...
|
||||
oo_, initialguess);
|
||||
if ~info_convergence
|
||||
msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s)!', int2str(t));
|
||||
warning(msg)
|
||||
|
@ -86,13 +86,13 @@ end % (while) loop over t
|
|||
dyn_waitbar_close(hh_fig);
|
||||
|
||||
% Set the initial period.
|
||||
if isdates(DynareOptions.initial_period)
|
||||
if ischar(DynareOptions.initial_period)
|
||||
initial_period = dates(DynareOptions.initial_period);
|
||||
if isdates(options_.initial_period)
|
||||
if ischar(options_.initial_period)
|
||||
initial_period = dates(options_.initial_period);
|
||||
else
|
||||
initial_period = DynareOptions.initial_period;
|
||||
initial_period = options_.initial_period;
|
||||
end
|
||||
elseif isnan(DynareOptions.initial_period)
|
||||
elseif isnan(options_.initial_period)
|
||||
initial_period = dates(1,1);
|
||||
else
|
||||
error('Type of option initial_period is wrong.')
|
||||
|
@ -104,11 +104,11 @@ if any(isnan(endogenous_variables_paths(:)))
|
|||
nn = size(endogenous_variables_paths, 1);
|
||||
endogenous_variables_paths = reshape(endogenous_variables_paths(sl), nn, length(sl)/nn);
|
||||
end
|
||||
ts = dseries(transpose(endogenous_variables_paths), initial_period, DynareModel.endo_names);
|
||||
ts = dseries(transpose(endogenous_variables_paths), initial_period, M_.endo_names);
|
||||
|
||||
DynareResults.endo_simul = transpose(ts.data);
|
||||
oo_.endo_simul = transpose(ts.data);
|
||||
assignin('base', 'Simulated_time_series', ts);
|
||||
|
||||
if ~nargout || nargout<2
|
||||
assignin('base', 'oo_', DynareResults);
|
||||
assignin('base', 'oo_', oo_);
|
||||
end
|
||||
|
|
|
@ -1,8 +1,8 @@
|
|||
function [y, info_convergence, endogenousvariablespaths] = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
|
||||
exo_simul,init,initial_conditions,...
|
||||
steady_state, ...
|
||||
debug,order,M,pfm,algo,solve_algo,stack_solve_algo,...
|
||||
olmmcp,options,oo,initialguess)
|
||||
debug,order,M_,pfm,algo,solve_algo,stack_solve_algo,...
|
||||
olmmcp,options_,oo_,initialguess)
|
||||
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
|
@ -21,10 +21,10 @@ function [y, info_convergence, endogenousvariablespaths] = extended_path_core(pe
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
ep = options.ep;
|
||||
ep = options_.ep;
|
||||
|
||||
if init% Compute first order solution (Perturbation)...
|
||||
endo_simul = simult_(M,options,initial_conditions,oo.dr,exo_simul(2:end,:),1);
|
||||
endo_simul = simult_(M_,options_,initial_conditions,oo_.dr,exo_simul(2:end,:),1);
|
||||
else
|
||||
if nargin==19 && ~isempty(initialguess)
|
||||
% Note that the first column of initialguess should be equal to initial_conditions.
|
||||
|
@ -34,29 +34,29 @@ else
|
|||
end
|
||||
end
|
||||
|
||||
oo.endo_simul = endo_simul;
|
||||
oo_.endo_simul = endo_simul;
|
||||
|
||||
if debug
|
||||
save ep_test_1.mat endo_simul exo_simul
|
||||
end
|
||||
|
||||
if options.bytecode && order > 0
|
||||
if options_.bytecode && order > 0
|
||||
error('Option order > 0 of extended_path command is not compatible with bytecode option.')
|
||||
end
|
||||
if options.block && order > 0
|
||||
if options_.block && order > 0
|
||||
error('Option order > 0 of extended_path command is not compatible with block option.')
|
||||
end
|
||||
|
||||
if order == 0
|
||||
options.periods = periods;
|
||||
options.block = pfm.block;
|
||||
oo.endo_simul = endo_simul;
|
||||
oo.exo_simul = exo_simul;
|
||||
oo.steady_state = steady_state;
|
||||
options.lmmcp = olmmcp;
|
||||
options.solve_algo = solve_algo;
|
||||
options.stack_solve_algo = stack_solve_algo;
|
||||
[endogenousvariablespaths, info_convergence] = perfect_foresight_solver_core(oo.endo_simul, oo.exo_simul, oo.steady_state, oo.exo_steady_state, M, options);
|
||||
options_.periods = periods;
|
||||
options_.block = pfm.block;
|
||||
oo_.endo_simul = endo_simul;
|
||||
oo_.exo_simul = exo_simul;
|
||||
oo_.steady_state = steady_state;
|
||||
options_.lmmcp = olmmcp;
|
||||
options_.solve_algo = solve_algo;
|
||||
options_.stack_solve_algo = stack_solve_algo;
|
||||
[endogenousvariablespaths, info_convergence] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
|
||||
else
|
||||
switch(algo)
|
||||
case 0
|
||||
|
@ -64,13 +64,13 @@ else
|
|||
solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
|
||||
case 1
|
||||
[flag, endogenousvariablespaths] = ...
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options, pfm, ep.stochastic.order);
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
|
||||
end
|
||||
info_convergence = ~flag;
|
||||
end
|
||||
|
||||
if ~info_convergence && ~options.no_homotopy
|
||||
[info_convergence, endogenousvariablespaths] = extended_path_homotopy(endo_simul, exo_simul, M, options, oo, pfm, ep, order, algo, 2, debug);
|
||||
if ~info_convergence && ~options_.no_homotopy
|
||||
[info_convergence, endogenousvariablespaths] = extended_path_homotopy(endo_simul, exo_simul, M_, options_, oo_, pfm, ep, order, algo, 2, debug);
|
||||
end
|
||||
|
||||
if info_convergence
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo_simul, M, options, oo, pfm, ep, order, algo, method, debug)
|
||||
function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo_simul, M_, options_, oo_, pfm, ep, order, algo, method, debug)
|
||||
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
|
@ -31,11 +31,11 @@ if ismember(method, [1, 2])
|
|||
oldweight = weight;
|
||||
while noconvergence
|
||||
iteration = iteration + 1;
|
||||
oo.endo_simul = endo_simul;
|
||||
oo.endo_simul(:,1) = oo.steady_state + weight*(endo_simul0(:,1) - oo.steady_state);
|
||||
oo.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo.exo_steady_state));
|
||||
oo_.endo_simul = endo_simul;
|
||||
oo_.endo_simul(:,1) = oo_.steady_state + weight*(endo_simul0(:,1) - oo_.steady_state);
|
||||
oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
|
||||
if order==0
|
||||
[endo_simul_new, success] = perfect_foresight_solver_core(oo.endo_simul, oo.exo_simul, oo.steady_state, oo.exo_steady_state, M, options);
|
||||
[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
|
||||
else
|
||||
switch(algo)
|
||||
case 0
|
||||
|
@ -43,7 +43,7 @@ if ismember(method, [1, 2])
|
|||
solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
|
||||
case 1
|
||||
[flag, endo_simul_new] = ...
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options, pfm, ep.stochastic.order);
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
|
||||
end
|
||||
end
|
||||
if isequal(order, 0)
|
||||
|
@ -99,10 +99,10 @@ if isequal(method, 3) || (isequal(method, 2) && noconvergence)
|
|||
nweights = length(weights);
|
||||
while noconvergence
|
||||
weight = weights(index);
|
||||
oo.endo_simul = endo_simul;
|
||||
oo.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo.exo_steady_state));
|
||||
oo_.endo_simul = endo_simul;
|
||||
oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
|
||||
if order==0
|
||||
[endo_simul_new, success] = perfect_foresight_solver_core(oo.endo_simul, oo.exo_simul, oo.steady_state, oo.exo_steady_state, M, options);
|
||||
[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
|
||||
else
|
||||
switch(algo)
|
||||
case 0
|
||||
|
@ -110,7 +110,7 @@ if isequal(method, 3) || (isequal(method, 2) && noconvergence)
|
|||
solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
|
||||
case 1
|
||||
[flag, endo_simul_new] = ...
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options, pfm, ep.stochastic.order);
|
||||
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
|
||||
end
|
||||
end
|
||||
if isequal(order, 0)
|
||||
|
|
|
@ -1,14 +1,14 @@
|
|||
function [initial_conditions, innovations, pfm, ep, verbosity, DynareOptions, DynareResults] = extended_path_initialization(initial_conditions, sample_size, exogenousvariables, DynareOptions, DynareModel, DynareResults)
|
||||
|
||||
function [initial_conditions, innovations, pfm, ep, verbosity, options_, oo_] = extended_path_initialization(initial_conditions, sample_size, exogenousvariables, options_, M_, oo_)
|
||||
% [initial_conditions, innovations, pfm, ep, verbosity, options_, oo_] = extended_path_initialization(initial_conditions, sample_size, exogenousvariables, options_, M_, oo_)
|
||||
% Initialization of the extended path routines.
|
||||
%
|
||||
% INPUTS
|
||||
% o initial_conditions [double] m*1 array, where m is the number of endogenous variables in the model.
|
||||
% o sample_size [integer] scalar, size of the sample to be simulated.
|
||||
% o exogenousvariables [double] T*n array, values for the structural innovations.
|
||||
% o DynareOptions [struct] options_
|
||||
% o DynareModel [struct] M_
|
||||
% o DynareResults [struct] oo_
|
||||
% o options_ [struct] Dynare's options structure
|
||||
% o M_ [struct] Dynare's model structure
|
||||
% o oo_ [struct] Dynare's result structure
|
||||
%
|
||||
% OUTPUTS
|
||||
%
|
||||
|
@ -16,7 +16,7 @@ function [initial_conditions, innovations, pfm, ep, verbosity, DynareOptions, Dy
|
|||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
|
||||
% Copyright © 2016-2020 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -33,29 +33,29 @@ function [initial_conditions, innovations, pfm, ep, verbosity, DynareOptions, Dy
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
ep = DynareOptions.ep;
|
||||
ep = options_.ep;
|
||||
|
||||
% Set verbosity levels.
|
||||
DynareOptions.verbosity = ep.verbosity;
|
||||
options_.verbosity = ep.verbosity;
|
||||
verbosity = ep.verbosity+ep.debug;
|
||||
|
||||
% Set maximum number of iterations for the deterministic solver.
|
||||
DynareOptions.simul.maxit = ep.maxit;
|
||||
options_.simul.maxit = ep.maxit;
|
||||
|
||||
% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
|
||||
pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel, DynareOptions, DynareResults);
|
||||
pfm = setup_stochastic_perfect_foresight_model_solver(M_, options_, oo_);
|
||||
|
||||
% Check that the user did not use varexo_det
|
||||
if DynareModel.exo_det_nbr~=0
|
||||
if M_.exo_det_nbr~=0
|
||||
error('Extended path does not support varexo_det.')
|
||||
end
|
||||
|
||||
% Set default initial conditions.
|
||||
if isempty(initial_conditions)
|
||||
if isempty(DynareModel.endo_histval)
|
||||
initial_conditions = DynareResults.steady_state;
|
||||
if isempty(M_.endo_histval)
|
||||
initial_conditions = oo_.steady_state;
|
||||
else
|
||||
initial_conditions = DynareModel.endo_histval;
|
||||
initial_conditions = M_.endo_histval;
|
||||
end
|
||||
end
|
||||
|
||||
|
@ -64,10 +64,10 @@ pfm.periods = ep.periods;
|
|||
|
||||
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
|
||||
|
||||
pfm.block = DynareOptions.block;
|
||||
pfm.block = options_.block;
|
||||
|
||||
% Set the algorithm for the perfect foresight solver
|
||||
DynareOptions.stack_solve_algo = ep.stack_solve_algo;
|
||||
options_.stack_solve_algo = ep.stack_solve_algo;
|
||||
|
||||
% Compute the first order reduced form if needed.
|
||||
%
|
||||
|
@ -76,20 +76,20 @@ DynareOptions.stack_solve_algo = ep.stack_solve_algo;
|
|||
|
||||
dr = struct();
|
||||
if ep.init
|
||||
DynareOptions.order = 1;
|
||||
DynareResults.dr=set_state_space(dr,DynareModel);
|
||||
[DynareResults.dr,Info,DynareModel.params] = resol(0,DynareModel,DynareOptions,DynareResults.dr,DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
|
||||
options_.order = 1;
|
||||
oo_.dr=set_state_space(dr,M_);
|
||||
[oo_.dr,Info,M_.params] = resol(0,M_,options_,oo_.dr,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
|
||||
end
|
||||
|
||||
% Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
|
||||
DynareOptions.minimal_solving_period = DynareOptions.ep.periods;
|
||||
options_.minimal_solving_period = options_.ep.periods;
|
||||
|
||||
% Set the covariance matrix of the structural innovations.
|
||||
if isempty(exogenousvariables)
|
||||
innovations = struct();
|
||||
innovations.positive_var_indx = find(diag(DynareModel.Sigma_e)>0);
|
||||
innovations.positive_var_indx = find(diag(M_.Sigma_e)>0);
|
||||
innovations.effective_number_of_shocks = length(innovations.positive_var_indx);
|
||||
innovations.covariance_matrix = DynareModel.Sigma_e(innovations.positive_var_indx,innovations.positive_var_indx);
|
||||
innovations.covariance_matrix = M_.Sigma_e(innovations.positive_var_indx,innovations.positive_var_indx);
|
||||
innovations.covariance_matrix_upper_cholesky = chol(innovations.covariance_matrix);
|
||||
else
|
||||
innovations = struct();
|
||||
|
@ -97,26 +97,26 @@ end
|
|||
|
||||
% Set seed.
|
||||
if ep.set_dynare_seed_to_default
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
end
|
||||
|
||||
% hybrid correction
|
||||
pfm.hybrid_order = ep.stochastic.hybrid_order;
|
||||
if pfm.hybrid_order
|
||||
DynareResults.dr = set_state_space(DynareResults.dr, DynareModel);
|
||||
options = DynareOptions;
|
||||
oo_.dr = set_state_space(oo_.dr, M_);
|
||||
options = options_;
|
||||
options.order = pfm.hybrid_order;
|
||||
[pfm.dr, DynareModel.params] = resol(0, DynareModel, options, DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
|
||||
[pfm.dr, M_.params] = resol(0, M_, options, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
|
||||
else
|
||||
pfm.dr = [];
|
||||
end
|
||||
|
||||
% number of nonzero derivatives
|
||||
pfm.nnzA = DynareModel.NNZDerivatives(1);
|
||||
pfm.nnzA = M_.NNZDerivatives(1);
|
||||
|
||||
% setting up integration nodes if order > 0
|
||||
if ep.stochastic.order > 0
|
||||
[nodes,weights,nnodes] = setup_integration_nodes(DynareOptions.ep,pfm);
|
||||
[nodes,weights,nnodes] = setup_integration_nodes(options_.ep,pfm);
|
||||
pfm.nodes = nodes;
|
||||
pfm.weights = weights;
|
||||
pfm.nnodes = nnodes;
|
||||
|
@ -127,12 +127,12 @@ else
|
|||
end
|
||||
|
||||
% set boundaries if mcp
|
||||
[lb,ub,pfm.eq_index] = get_complementarity_conditions(DynareModel, DynareOptions.ramsey_policy);
|
||||
if DynareOptions.ep.solve_algo == 10
|
||||
DynareOptions.lmmcp.lb = repmat(lb,block_nbr,1);
|
||||
DynareOptions.lmmcp.ub = repmat(ub,block_nbr,1);
|
||||
elseif DynareOptions.ep.solve_algo == 11
|
||||
DynareOptions.mcppath.lb = repmat(lb,block_nbr,1);
|
||||
DynareOptions.mcppath.ub = repmat(ub,block_nbr,1);
|
||||
[lb,ub,pfm.eq_index] = get_complementarity_conditions(M_, options_.ramsey_policy);
|
||||
if options_.ep.solve_algo == 10
|
||||
options_.lmmcp.lb = repmat(lb,block_nbr,1);
|
||||
options_.lmmcp.ub = repmat(ub,block_nbr,1);
|
||||
elseif options_.ep.solve_algo == 11
|
||||
options_.mcppath.lb = repmat(lb,block_nbr,1);
|
||||
options_.mcppath.ub = repmat(ub,block_nbr,1);
|
||||
end
|
||||
pfm.block_nbr = block_nbr;
|
||||
|
|
|
@ -1,15 +1,15 @@
|
|||
function Simulations = extended_path_mc(initialconditions, samplesize, replic, exogenousvariables, DynareOptions, DynareModel, DynareResults)
|
||||
|
||||
function Simulations = extended_path_mc(initialconditions, samplesize, replic, exogenousvariables, options_, M_, oo_)
|
||||
% Simulations = extended_path_mc(initialconditions, samplesize, replic, exogenousvariables, options_, M_, oo_)
|
||||
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
|
||||
% series of size T is obtained by solving T perfect foresight models.
|
||||
%
|
||||
% INPUTS
|
||||
% o initialconditions [double] m*1 array, where m is the number of endogenous variables in the model.
|
||||
% o samplesize [integer] scalar, size of the sample to be simulated.
|
||||
% o samplesize [integer] scalar, size of the sample to be simulated.
|
||||
% o exogenousvariables [double] T*n array, values for the structural innovations.
|
||||
% o DynareOptions [struct] options_
|
||||
% o DynareModel [struct] M_
|
||||
% o DynareResults [struct] oo_
|
||||
% o options_ [struct] Dynare's options structure
|
||||
% o M_ [struct] Dynare's model structure
|
||||
% o oo_ [struct] Dynare's results structure
|
||||
%
|
||||
% OUTPUTS
|
||||
% o ts [dseries] m*samplesize array, the simulations.
|
||||
|
@ -19,7 +19,7 @@ function Simulations = extended_path_mc(initialconditions, samplesize, replic, e
|
|||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
|
||||
% Copyright © 2016-2020 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -36,8 +36,8 @@ function Simulations = extended_path_mc(initialconditions, samplesize, replic, e
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
[initialconditions, innovations, pfm, ep, verbosity, DynareOptions, DynareResults] = ...
|
||||
extended_path_initialization(initialconditions, samplesize, exogenousvariables, DynareOptions, DynareModel, DynareResults);
|
||||
[initialconditions, innovations, pfm, ep, verbosity, options_, oo_] = ...
|
||||
extended_path_initialization(initialconditions, samplesize, exogenousvariables, options_, M_, oo_);
|
||||
|
||||
% Check the dimension of the first input argument
|
||||
if isequal(size(initialconditions, 2), 1)
|
||||
|
@ -68,9 +68,9 @@ if ep.parallel
|
|||
% Use the Parallel toolbox.
|
||||
parfor i=1:replic
|
||||
innovations_ = innovations;
|
||||
DynareResults_ = DynareResults;
|
||||
[shocks, spfm_exo_simul, innovations_, DynareResults_] = extended_path_shocks(innovations_, ep, exogenousvariables(:,:,i), samplesize, DynareModel, DynareOptions, DynareResults_);
|
||||
endogenous_variables_paths = NaN(DynareModel.endo_nbr,samplesize+1);
|
||||
oo__ = oo_;
|
||||
[shocks, spfm_exo_simul, innovations_, oo__] = extended_path_shocks(innovations_, ep, exogenousvariables(:,:,i), samplesize, M_, options_, oo__);
|
||||
endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
|
||||
endogenous_variables_paths(:,1) = initialconditions(:,1);
|
||||
exogenous_variables_paths = NaN(innovations_.effective_number_of_shocks,samplesize+1);
|
||||
exogenous_variables_paths(:,1) = 0;
|
||||
|
@ -80,12 +80,12 @@ if ep.parallel
|
|||
t = t+1;
|
||||
spfm_exo_simul(2,:) = shocks(t-1,:);
|
||||
exogenous_variables_paths(:,t) = shocks(t-1,:);
|
||||
[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, DynareModel.endo_nbr, DynareModel.exo_nbr, innovations_.positive_var_indx, ...
|
||||
[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, innovations_.positive_var_indx, ...
|
||||
spfm_exo_simul, ep.init, endogenous_variables_paths(:,t-1), ...
|
||||
DynareResults_.steady_state, ...
|
||||
oo__.steady_state, ...
|
||||
ep.verbosity, ep.stochastic.order, ...
|
||||
DynareModel, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
DynareOptions.lmmcp, DynareOptions, DynareResults_);
|
||||
M_, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
options_.lmmcp, options_, oo__);
|
||||
if ~info_convergence
|
||||
msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s, iteration %s)!', int2str(t), int2str(i));
|
||||
warning(msg)
|
||||
|
@ -99,8 +99,8 @@ if ep.parallel
|
|||
else
|
||||
% Sequential approach.
|
||||
for i=1:replic
|
||||
[shocks, spfm_exo_simul, innovations, DynareResults] = extended_path_shocks(innovations, ep, exogenousvariables(:,:,i), samplesize, DynareModel, DynareOptions, DynareResults);
|
||||
endogenous_variables_paths = NaN(DynareModel.endo_nbr,samplesize+1);
|
||||
[shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables(:,:,i), samplesize, M_, options_, oo_);
|
||||
endogenous_variables_paths = NaN(M_.endo_nbr,samplesize+1);
|
||||
endogenous_variables_paths(:,1) = initialconditions(:,1);
|
||||
exogenous_variables_paths = NaN(innovations.effective_number_of_shocks,samplesize+1);
|
||||
exogenous_variables_paths(:,1) = 0;
|
||||
|
@ -109,12 +109,12 @@ else
|
|||
t = t+1;
|
||||
spfm_exo_simul(2,:) = shocks(t-1,:);
|
||||
exogenous_variables_paths(:,t) = shocks(t-1,:);
|
||||
[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, DynareModel.endo_nbr, DynareModel.exo_nbr, innovations.positive_var_indx, ...
|
||||
[endogenous_variables_paths(:,t), info_convergence] = extended_path_core(ep.periods, M_.endo_nbr, M_.exo_nbr, innovations.positive_var_indx, ...
|
||||
spfm_exo_simul, ep.init, endogenous_variables_paths(:,t-1), ...
|
||||
DynareResults.steady_state, ...
|
||||
oo_.steady_state, ...
|
||||
ep.verbosity, ep.stochastic.order, ...
|
||||
DynareModel, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
DynareOptions.lmmcp, DynareOptions, DynareResults);
|
||||
M_, pfm,ep.stochastic.algo, ep.solve_algo, ep.stack_solve_algo, ...
|
||||
options_.lmmcp, options_, oo_);
|
||||
if ~info_convergence
|
||||
msg = sprintf('No convergence of the (stochastic) perfect foresight solver (in period %s, iteration %s)!', int2str(t), int2str(i));
|
||||
warning(msg)
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
function [shocks, spfm_exo_simul, innovations, DynareResults] = extended_path_shocks(innovations, ep, exogenousvariables, sample_size,DynareModel,DynareOptions, DynareResults)
|
||||
|
||||
% Copyright © 2016-2017 Dynare Team
|
||||
function [shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables, sample_size,M_,options_, oo_)
|
||||
% [shocks, spfm_exo_simul, innovations, oo_] = extended_path_shocks(innovations, ep, exogenousvariables, sample_size,M_,options_, oo_)
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -21,13 +21,13 @@ function [shocks, spfm_exo_simul, innovations, DynareResults] = extended_path_sh
|
|||
if isempty(exogenousvariables)
|
||||
switch ep.innovation_distribution
|
||||
case 'gaussian'
|
||||
shocks = zeros(sample_size, DynareModel.exo_nbr);
|
||||
shocks = zeros(sample_size, M_.exo_nbr);
|
||||
shocks(:,innovations.positive_var_indx) = transpose(transpose(innovations.covariance_matrix_upper_cholesky)*randn(innovations.effective_number_of_shocks,sample_size));
|
||||
case 'calibrated'
|
||||
options = DynareOptions;
|
||||
options = options_;
|
||||
options.periods = options.ep.periods;
|
||||
oo = make_ex_(DynareModel,options,DynareResults);
|
||||
shocks = oo.exo_simul(2:end,:);
|
||||
oo_local = make_ex_(M_,options,oo_);
|
||||
shocks = oo_local.exo_simul(2:end,:);
|
||||
otherwise
|
||||
error(['extended_path:: ' ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
|
||||
end
|
||||
|
@ -37,5 +37,5 @@ else
|
|||
end
|
||||
|
||||
% Copy the shocks in exo_simul
|
||||
DynareResults.exo_simul = shocks;
|
||||
spfm_exo_simul = repmat(DynareResults.exo_steady_state',ep.periods+2,1);
|
||||
oo_.exo_simul = shocks;
|
||||
spfm_exo_simul = repmat(oo_.exo_steady_state',ep.periods+2,1);
|
|
@ -1,6 +1,11 @@
|
|||
function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,DynareOptions,DynareOutput)
|
||||
function pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_)
|
||||
% pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_)
|
||||
% INPUTS
|
||||
% o M_ [struct] Dynare's model structure
|
||||
% o options_ [struct] Dynare's options structure
|
||||
% o oo_ [struct] Dynare's results structure
|
||||
|
||||
% Copyright © 2013-2020 Dynare Team
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -17,15 +22,15 @@ function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,Dynar
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
|
||||
pfm.ny = DynareModel.endo_nbr;
|
||||
pfm.Sigma = DynareModel.Sigma_e;
|
||||
pfm.lead_lag_incidence = M_.lead_lag_incidence;
|
||||
pfm.ny = M_.endo_nbr;
|
||||
pfm.Sigma = M_.Sigma_e;
|
||||
if det(pfm.Sigma) > 0
|
||||
pfm.Omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
|
||||
end
|
||||
pfm.number_of_shocks = length(pfm.Sigma);
|
||||
pfm.stochastic_order = DynareOptions.ep.stochastic.order;
|
||||
pfm.max_lag = DynareModel.maximum_endo_lag;
|
||||
pfm.stochastic_order = options_.ep.stochastic.order;
|
||||
pfm.max_lag = M_.maximum_endo_lag;
|
||||
if pfm.max_lag > 0
|
||||
pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
|
||||
pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
|
||||
|
@ -35,7 +40,7 @@ else
|
|||
end
|
||||
pfm.ny0 = nnz(pfm.lead_lag_incidence(pfm.max_lag+1,:));
|
||||
pfm.iy0 = find(pfm.lead_lag_incidence(pfm.max_lag+1,:)>0);
|
||||
if DynareModel.maximum_endo_lead
|
||||
if M_.maximum_endo_lead
|
||||
pfm.nyf = nnz(pfm.lead_lag_incidence(pfm.max_lag+2,:));
|
||||
pfm.iyf = find(pfm.lead_lag_incidence(pfm.max_lag+2,:)>0);
|
||||
else
|
||||
|
@ -49,10 +54,10 @@ pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
|
|||
pfm.isf = pfm.iyf+pfm.nyp;
|
||||
pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
|
||||
pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
|
||||
pfm.periods = DynareOptions.ep.periods;
|
||||
pfm.steady_state = DynareOutput.steady_state;
|
||||
pfm.params = DynareModel.params;
|
||||
if DynareModel.maximum_endo_lead
|
||||
pfm.periods = options_.ep.periods;
|
||||
pfm.steady_state = oo_.steady_state;
|
||||
pfm.params = M_.params;
|
||||
if M_.maximum_endo_lead
|
||||
pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
|
||||
pfm.i_cols_A1 = find(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
|
||||
else
|
||||
|
@ -66,9 +71,9 @@ else
|
|||
end
|
||||
pfm.i_cols_j = 1:pfm.nd;
|
||||
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
|
||||
if ~DynareOptions.bytecode
|
||||
pfm.dynamic_model = str2func([DynareModel.fname,'.dynamic']);
|
||||
if ~options_.bytecode
|
||||
pfm.dynamic_model = str2func([M_.fname,'.dynamic']);
|
||||
end
|
||||
pfm.verbose = DynareOptions.ep.verbosity;
|
||||
pfm.maxit_ = DynareOptions.simul.maxit;
|
||||
pfm.tolerance = DynareOptions.dynatol.f;
|
||||
pfm.verbose = options_.ep.verbosity;
|
||||
pfm.maxit_ = options_.simul.maxit;
|
||||
pfm.tolerance = options_.dynatol.f;
|
||||
|
|
|
@ -1,15 +1,15 @@
|
|||
function eqnumber = get_equation_number_by_tag(eqname, M_)
|
||||
|
||||
% eqnumber = get_equation_number_by_tag(eqname, M_)
|
||||
% Translates an equation name into an equation number.
|
||||
%
|
||||
% INPUTS
|
||||
% - eqname [char] 1×n array, name of the equation.
|
||||
% - DynareModel [struct] Structure describing the model, aka M_.
|
||||
% - M_ [struct] Structure describing the model
|
||||
%
|
||||
% OUTPUTS
|
||||
% - eqnumber [integer] Equation number.
|
||||
|
||||
% Copyright © 2018-2022 Dynare Team
|
||||
% Copyright © 2018-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
|
@ -1,10 +1,10 @@
|
|||
function [lhs, rhs, json] = get_lhs_and_rhs(eqname, DynareModel, original, json)
|
||||
|
||||
function [lhs, rhs, json] = get_lhs_and_rhs(eqname, M_, original, json)
|
||||
% [lhs, rhs, json] = get_lhs_and_rhs(eqname, M_, original, json)
|
||||
% Returns the left and right handsides of an equation.
|
||||
%
|
||||
% INPUTS
|
||||
% - eqname [char] Name of the equation.
|
||||
% - DynareModel [struct] Structure describing the current model (M_).
|
||||
% - M_ [struct] Structure describing the current model.
|
||||
% - original [logical] fetch equation in modfile-original.json or modfile.json
|
||||
% - json [char] content of the JSON file
|
||||
%
|
||||
|
@ -23,7 +23,7 @@ function [lhs, rhs, json] = get_lhs_and_rhs(eqname, DynareModel, original, json)
|
|||
% [name='Phillips curve']
|
||||
% pi = beta*pi(1) + slope*y + lam;
|
||||
|
||||
% Copyright © 2018-2020 Dynare Team
|
||||
% Copyright © 2018-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -47,9 +47,9 @@ end
|
|||
% Load JSON file if nargin<4
|
||||
if nargin<4
|
||||
if original
|
||||
json = loadjson_([DynareModel.fname filesep() 'model' filesep() 'json' filesep() 'modfile-original.json']);
|
||||
json = loadjson_([M_.fname filesep() 'model' filesep() 'json' filesep() 'modfile-original.json']);
|
||||
else
|
||||
json = loadjson_([DynareModel.fname filesep() 'model' filesep() 'json' filesep() 'modfile.json']);
|
||||
json = loadjson_([M_.fname filesep() 'model' filesep() 'json' filesep() 'modfile.json']);
|
||||
end
|
||||
end
|
||||
|
||||
|
|
|
@ -1,11 +1,11 @@
|
|||
function [pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation(lhs, rhs, DynareModel)
|
||||
|
||||
function [pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation(lhs, rhs, M_)
|
||||
% [pnames, enames, xnames, pid, eid, xid] = get_variables_and_parameters_in_equation(lhs, rhs, M_)
|
||||
% Returns the lists of parameters, endogenous variables and exogenous variables in an equation.
|
||||
%
|
||||
% INPUTS
|
||||
% - lhs [string] Left hand side of an equation.
|
||||
% - rhs [string] Right hand side of an equation.
|
||||
% - DynareModel [struct] Structure describing the current model (M_).
|
||||
% - M_ [struct] Structure describing the current model.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - pnames [cell] Cell of row char arrays (p elements), names of the parameters.
|
||||
|
@ -39,33 +39,33 @@ rhs_ = get_variables_and_parameters_in_expression(rhs);
|
|||
lhs_ = get_variables_and_parameters_in_expression(lhs);
|
||||
|
||||
% Get list of parameters.
|
||||
pnames = DynareModel.param_names;
|
||||
pnames = M_.param_names;
|
||||
pnames = intersect([rhs_, lhs_], pnames);
|
||||
|
||||
if nargout>1
|
||||
% Get list of endogenous variables.
|
||||
enames = DynareModel.endo_names;
|
||||
enames = M_.endo_names;
|
||||
enames = intersect([rhs_, lhs_], enames);
|
||||
if nargout>2
|
||||
% Get list of exogenous variables
|
||||
xnames = DynareModel.exo_names;
|
||||
xnames = M_.exo_names;
|
||||
xnames = intersect([rhs_,lhs_], xnames);
|
||||
if nargout>3
|
||||
% Returns vector of indices for parameters endogenous and exogenous variables if required.
|
||||
p = length(pnames);
|
||||
pid = zeros(p, 1);
|
||||
for i = 1:p
|
||||
pid(i) = find(strcmp(pnames{i}, DynareModel.param_names));
|
||||
pid(i) = find(strcmp(pnames{i}, M_.param_names));
|
||||
end
|
||||
p = length(enames);
|
||||
eid = zeros(p, 1);
|
||||
for i = 1:p
|
||||
eid(i) = find(strcmp(enames{i}, DynareModel.endo_names));
|
||||
eid(i) = find(strcmp(enames{i}, M_.endo_names));
|
||||
end
|
||||
p = length(xnames);
|
||||
xid = zeros(p, 1);
|
||||
for i = 1:p
|
||||
xid(i) = find(strcmp(xnames{i}, DynareModel.exo_names));
|
||||
xid(i) = find(strcmp(xnames{i}, M_.exo_names));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
|
|
@ -1,5 +1,6 @@
|
|||
function map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResults, EstimatedParameters, BayesInfo)
|
||||
% map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResults, EstimatedParameters, BayesInfo)
|
||||
function map_calibration(OutputDirectoryName, M_, options_, oo_, estim_params_, bayestopt_)
|
||||
% map_calibration(OutputDirectoryName, M_, options_, oo_, estim_params_, bayestopt_)
|
||||
|
||||
|
||||
% Written by Marco Ratto
|
||||
% Joint Research Centre, The European Commission,
|
||||
|
@ -23,33 +24,33 @@ function map_calibration(OutputDirectoryName, Model, DynareOptions, DynareResult
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
fname_ = Model.fname;
|
||||
fname_ = M_.fname;
|
||||
|
||||
np = EstimatedParameters.np;
|
||||
nshock = EstimatedParameters.nvx + EstimatedParameters.nvn + EstimatedParameters.ncx + EstimatedParameters.ncn;
|
||||
np = estim_params_.np;
|
||||
nshock = estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
|
||||
pnames=cell(np,1);
|
||||
pnames_tex=cell(np,1);
|
||||
for jj=1:np
|
||||
if DynareOptions.TeX
|
||||
[param_name_temp, param_name_tex_temp]= get_the_name(nshock+jj, DynareOptions.TeX, Model, EstimatedParameters, DynareOptions);
|
||||
if options_.TeX
|
||||
[param_name_temp, param_name_tex_temp]= get_the_name(nshock+jj, options_.TeX, M_, estim_params_, options_);
|
||||
pnames_tex{jj,1} = strrep(param_name_tex_temp,'$','');
|
||||
pnames{jj,1} = param_name_temp;
|
||||
else
|
||||
param_name_temp = get_the_name(nshock+jj, DynareOptions.TeX, Model, EstimatedParameters, DynareOptions);
|
||||
param_name_temp = get_the_name(nshock+jj, options_.TeX, M_, estim_params_, options_);
|
||||
pnames{jj,1} = param_name_temp;
|
||||
end
|
||||
end
|
||||
|
||||
pvalue_ks = DynareOptions.opt_gsa.pvalue_ks;
|
||||
pvalue_ks = options_.opt_gsa.pvalue_ks;
|
||||
indx_irf = [];
|
||||
indx_moment = [];
|
||||
init = ~DynareOptions.opt_gsa.load_stab;
|
||||
init = ~options_.opt_gsa.load_stab;
|
||||
|
||||
options_mcf.pvalue_ks = DynareOptions.opt_gsa.pvalue_ks;
|
||||
options_mcf.pvalue_corr = DynareOptions.opt_gsa.pvalue_corr;
|
||||
options_mcf.alpha2 = DynareOptions.opt_gsa.alpha2_stab;
|
||||
options_mcf.pvalue_ks = options_.opt_gsa.pvalue_ks;
|
||||
options_mcf.pvalue_corr = options_.opt_gsa.pvalue_corr;
|
||||
options_mcf.alpha2 = options_.opt_gsa.alpha2_stab;
|
||||
options_mcf.param_names = pnames;
|
||||
if DynareOptions.TeX
|
||||
if options_.TeX
|
||||
options_mcf.param_names_tex = pnames_tex;
|
||||
end
|
||||
options_mcf.fname_ = fname_;
|
||||
|
@ -58,17 +59,17 @@ options_mcf.OutputDirectoryName = OutputDirectoryName;
|
|||
skipline()
|
||||
disp('Sensitivity analysis for calibration criteria')
|
||||
|
||||
if DynareOptions.opt_gsa.ppost
|
||||
filetoload=dir([Model.dname filesep 'metropolis' filesep fname_ '_param_irf*.mat']);
|
||||
if options_.opt_gsa.ppost
|
||||
filetoload=dir([M_.dname filesep 'metropolis' filesep fname_ '_param_irf*.mat']);
|
||||
lpmat=[];
|
||||
for j=1:length(filetoload)
|
||||
load([Model.dname filesep 'metropolis' filesep fname_ '_param_irf',int2str(j),'.mat'])
|
||||
load([M_.dname filesep 'metropolis' filesep fname_ '_param_irf',int2str(j),'.mat'])
|
||||
lpmat = [lpmat; stock];
|
||||
clear stock
|
||||
end
|
||||
type = 'post';
|
||||
else
|
||||
if DynareOptions.opt_gsa.pprior
|
||||
if options_.opt_gsa.pprior
|
||||
filetoload=[OutputDirectoryName '/' fname_ '_prior'];
|
||||
load(filetoload,'lpmat','lpmat0','istable','iunstable','iindeterm','iwrong' ,'infox')
|
||||
lpmat = [lpmat0 lpmat];
|
||||
|
@ -84,31 +85,31 @@ end
|
|||
npar = size(pnames,1);
|
||||
nshock = np - npar;
|
||||
|
||||
nbr_irf_restrictions = size(DynareOptions.endogenous_prior_restrictions.irf,1);
|
||||
nbr_moment_restrictions = size(DynareOptions.endogenous_prior_restrictions.moment,1);
|
||||
nbr_irf_restrictions = size(options_.endogenous_prior_restrictions.irf,1);
|
||||
nbr_moment_restrictions = size(options_.endogenous_prior_restrictions.moment,1);
|
||||
|
||||
if init
|
||||
mat_irf=cell(nbr_irf_restrictions,1);
|
||||
for ij=1:nbr_irf_restrictions
|
||||
mat_irf{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.irf{ij,3}));
|
||||
mat_irf{ij}=NaN(Nsam,length(options_.endogenous_prior_restrictions.irf{ij,3}));
|
||||
end
|
||||
|
||||
mat_moment=cell(nbr_moment_restrictions,1);
|
||||
for ij=1:nbr_moment_restrictions
|
||||
mat_moment{ij}=NaN(Nsam,length(DynareOptions.endogenous_prior_restrictions.moment{ij,3}));
|
||||
mat_moment{ij}=NaN(Nsam,length(options_.endogenous_prior_restrictions.moment{ij,3}));
|
||||
end
|
||||
|
||||
irestrictions = [1:Nsam];
|
||||
h = dyn_waitbar(0,'Please wait...');
|
||||
for j=1:Nsam
|
||||
Model = set_all_parameters(lpmat(j,:)',EstimatedParameters,Model);
|
||||
M_ = set_all_parameters(lpmat(j,:)',estim_params_,M_);
|
||||
if nbr_moment_restrictions
|
||||
[Tt,Rr,SteadyState,info,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
|
||||
[Tt,Rr,SteadyState,info,oo_.dr, M_.params] = dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
|
||||
else
|
||||
[Tt,Rr,SteadyState,info,DynareResults.dr, Model.params] = dynare_resolve(Model,DynareOptions,DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state,'restrict');
|
||||
[Tt,Rr,SteadyState,info,oo_.dr, M_.params] = dynare_resolve(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state,'restrict');
|
||||
end
|
||||
if info(1)==0
|
||||
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,Model,DynareOptions,DynareResults.dr,DynareResults.steady_state,DynareResults.exo_steady_state,DynareResults.exo_det_steady_state);
|
||||
[info, info_irf, info_moment, data_irf, data_moment]=endogenous_prior_restrictions(Tt,Rr,M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
|
||||
if ~isempty(info_irf)
|
||||
for ij=1:nbr_irf_restrictions
|
||||
mat_irf{ij}(j,:)=data_irf{ij}(:,2)';
|
||||
|
@ -131,7 +132,7 @@ if init
|
|||
irestrictions=irestrictions(find(irestrictions));
|
||||
xmat=lpmat(irestrictions,:);
|
||||
skipline()
|
||||
endo_prior_restrictions=DynareOptions.endogenous_prior_restrictions;
|
||||
endo_prior_restrictions=options_.endogenous_prior_restrictions;
|
||||
save([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
|
||||
else
|
||||
load([OutputDirectoryName,filesep,fname_,'_',type,'_restrictions'],'xmat','mat_irf','mat_moment','irestrictions','indx_irf','indx_moment','endo_prior_restrictions');
|
||||
|
@ -190,8 +191,8 @@ if ~isempty(indx_irf)
|
|||
iplot_indx = ones(size(plot_indx));
|
||||
|
||||
indx_irf = indx_irf(irestrictions,:);
|
||||
if ~DynareOptions.nograph
|
||||
h1=dyn_figure(DynareOptions.nodisplay,'name',[type ' evaluation of irf restrictions']);
|
||||
if ~options_.nograph
|
||||
h1=dyn_figure(options_.nodisplay,'name',[type ' evaluation of irf restrictions']);
|
||||
nrow=ceil(sqrt(nbr_irf_couples));
|
||||
ncol=nrow;
|
||||
if nrow*(nrow-1)>nbr_irf_couples
|
||||
|
@ -204,7 +205,7 @@ if ~isempty(indx_irf)
|
|||
indx_irf_matrix(:,plot_indx(ij)) = indx_irf_matrix(:,plot_indx(ij)) + indx_irf(:,ij);
|
||||
for ik=1:size(mat_irf{ij},2)
|
||||
[Mean,Median,Var,HPD,Distrib] = ...
|
||||
posterior_moments(mat_irf{ij}(:,ik),0,DynareOptions.mh_conf_sig);
|
||||
posterior_moments(mat_irf{ij}(:,ik),0,options_.mh_conf_sig);
|
||||
irf_mean{plot_indx(ij)} = [irf_mean{plot_indx(ij)}; Mean];
|
||||
irf_median{plot_indx(ij)} = [irf_median{plot_indx(ij)}; Median];
|
||||
irf_var{plot_indx(ij)} = [irf_var{plot_indx(ij)}; Var];
|
||||
|
@ -218,7 +219,7 @@ if ~isempty(indx_irf)
|
|||
aleg = [aleg,'-' ,num2str(endo_prior_restrictions.irf{ij,3}(end))];
|
||||
iplot_indx(ij)=0;
|
||||
end
|
||||
if ~DynareOptions.nograph && length(time_matrix{plot_indx(ij)})==1
|
||||
if ~options_.nograph && length(time_matrix{plot_indx(ij)})==1
|
||||
set(0,'currentfigure',h1),
|
||||
subplot(nrow,ncol, plot_indx(ij)),
|
||||
hc = cumplot(mat_irf{ij}(:,ik));
|
||||
|
@ -258,7 +259,7 @@ if ~isempty(indx_irf)
|
|||
options_mcf.nobeha_title = 'NO IRF restriction';
|
||||
options_mcf.title = atitle0;
|
||||
if ~isempty(indx1) && ~isempty(indx2)
|
||||
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, DynareOptions);
|
||||
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, options_);
|
||||
end
|
||||
|
||||
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
|
||||
|
@ -269,7 +270,7 @@ if ~isempty(indx_irf)
|
|||
end
|
||||
for ij=1:nbr_irf_couples
|
||||
if length(time_matrix{ij})>1
|
||||
if ~DynareOptions.nograph
|
||||
if ~options_.nograph
|
||||
set(0,'currentfigure',h1);
|
||||
subplot(nrow,ncol, ij)
|
||||
itmp = (find(plot_indx==ij));
|
||||
|
@ -319,14 +320,14 @@ if ~isempty(indx_irf)
|
|||
options_mcf.nobeha_title = 'NO IRF restriction';
|
||||
options_mcf.title = atitle0;
|
||||
if ~isempty(indx1) && ~isempty(indx2)
|
||||
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, DynareOptions);
|
||||
mcf_analysis(xmat(:,nshock+1:end), indx1, indx2, options_mcf, options_);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
if ~DynareOptions.nograph
|
||||
dyn_saveas(h1,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],DynareOptions.nodisplay,DynareOptions.graph_format);
|
||||
create_TeX_loader(DynareOptions,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],[type ' evaluation of irf restrictions'],'irf_restrictions',type,DynareOptions.figures.textwidth*min(ij/ncol,1))
|
||||
if ~options_.nograph
|
||||
dyn_saveas(h1,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],options_.nodisplay,options_.graph_format);
|
||||
create_TeX_loader(options_,[OutputDirectoryName,filesep,fname_,'_',type,'_irf_restrictions'],[type ' evaluation of irf restrictions'],'irf_restrictions',type,options_.figures.textwidth*min(ij/ncol,1))
|
||||
end
|
||||
skipline()
|
||||
end
|
||||
|
@ -362,24 +363,24 @@ if ~isempty(indx_moment)
|
|||
skipline()
|
||||
|
||||
%get parameter names including standard deviations
|
||||
np=size(BayesInfo.name,1);
|
||||
np=size(bayestopt_.name,1);
|
||||
name=cell(np,1);
|
||||
name_tex=cell(np,1);
|
||||
for jj=1:np
|
||||
if DynareOptions.TeX
|
||||
[param_name_temp, param_name_tex_temp]= get_the_name(jj,DynareOptions.TeX,Model,EstimatedParameters,DynareOptions);
|
||||
if options_.TeX
|
||||
[param_name_temp, param_name_tex_temp]= get_the_name(jj,options_.TeX,M_,estim_params_,options_);
|
||||
name_tex{jj,1} = strrep(param_name_tex_temp,'$','');
|
||||
name{jj,1} = param_name_temp;
|
||||
else
|
||||
param_name_temp = get_the_name(jj,DynareOptions.TeX,Model,EstimatedParameters,DynareOptions);
|
||||
param_name_temp = get_the_name(jj,options_.TeX,M_,estim_params_,options_);
|
||||
name{jj,1} = param_name_temp;
|
||||
end
|
||||
end
|
||||
options_mcf.param_names = name;
|
||||
if DynareOptions.TeX
|
||||
if options_.TeX
|
||||
options_mcf.param_names_tex = name_tex;
|
||||
end
|
||||
options_mcf.param_names = BayesInfo.name;
|
||||
options_mcf.param_names = bayestopt_.name;
|
||||
all_moment_couples = cellstr([char(endo_prior_restrictions.moment(:,1)) char(endo_prior_restrictions.moment(:,2))]);
|
||||
moment_couples = unique(all_moment_couples);
|
||||
nbr_moment_couples = size(moment_couples,1);
|
||||
|
@ -405,8 +406,8 @@ if ~isempty(indx_moment)
|
|||
iplot_indx = ones(size(plot_indx));
|
||||
|
||||
indx_moment = indx_moment(irestrictions,:);
|
||||
if ~DynareOptions.nograph
|
||||
h2=dyn_figure(DynareOptions.nodisplay,'name',[type ' evaluation of moment restrictions']);
|
||||
if ~options_.nograph
|
||||
h2=dyn_figure(options_.nodisplay,'name',[type ' evaluation of moment restrictions']);
|
||||
nrow=ceil(sqrt(nbr_moment_couples));
|
||||
ncol=nrow;
|
||||
if nrow*(nrow-1)>nbr_moment_couples
|
||||
|
@ -420,7 +421,7 @@ if ~isempty(indx_moment)
|
|||
indx_moment_matrix(:,plot_indx(ij)) = indx_moment_matrix(:,plot_indx(ij)) + indx_moment(:,ij);
|
||||
for ik=1:size(mat_moment{ij},2)
|
||||
[Mean,Median,Var,HPD,Distrib] = ...
|
||||
posterior_moments(mat_moment{ij}(:,ik),0,DynareOptions.mh_conf_sig);
|
||||
posterior_moments(mat_moment{ij}(:,ik),0,options_.mh_conf_sig);
|
||||
moment_mean{plot_indx(ij)} = [moment_mean{plot_indx(ij)}; Mean];
|
||||
moment_median{plot_indx(ij)} = [moment_median{plot_indx(ij)}; Median];
|
||||
moment_var{plot_indx(ij)} = [moment_var{plot_indx(ij)}; Var];
|
||||
|
@ -434,7 +435,7 @@ if ~isempty(indx_moment)
|
|||
aleg = [aleg,'_' ,num2str(endo_prior_restrictions.moment{ij,3}(end))];
|
||||
iplot_indx(ij)=0;
|
||||
end
|
||||
if ~DynareOptions.nograph && length(time_matrix{plot_indx(ij)})==1
|
||||
if ~options_.nograph && length(time_matrix{plot_indx(ij)})==1
|
||||
set(0,'currentfigure',h2);
|
||||
subplot(nrow,ncol,plot_indx(ij)),
|
||||
hc = cumplot(mat_moment{ij}(:,ik));
|
||||
|
@ -469,7 +470,7 @@ if ~isempty(indx_moment)
|
|||
options_mcf.nobeha_title = 'NO moment restriction';
|
||||
options_mcf.title = atitle0;
|
||||
if ~isempty(indx1) && ~isempty(indx2)
|
||||
mcf_analysis(xmat, indx1, indx2, options_mcf, DynareOptions);
|
||||
mcf_analysis(xmat, indx1, indx2, options_mcf, options_);
|
||||
end
|
||||
|
||||
% [proba, dproba] = stab_map_1(xmat, indx1, indx2, aname, 0);
|
||||
|
@ -481,7 +482,7 @@ if ~isempty(indx_moment)
|
|||
for ij=1:nbr_moment_couples
|
||||
time_matrix{ij} = sort(time_matrix{ij});
|
||||
if length(time_matrix{ij})>1
|
||||
if ~DynareOptions.nograph
|
||||
if ~options_.nograph
|
||||
itmp = (find(plot_indx==ij));
|
||||
set(0,'currentfigure',h2);
|
||||
subplot(nrow,ncol, ij)
|
||||
|
@ -531,14 +532,14 @@ if ~isempty(indx_moment)
|
|||
options_mcf.nobeha_title = 'NO moment restriction';
|
||||
options_mcf.title = atitle0;
|
||||
if ~isempty(indx1) && ~isempty(indx2)
|
||||
mcf_analysis(xmat, indx1, indx2, options_mcf, DynareOptions);
|
||||
mcf_analysis(xmat, indx1, indx2, options_mcf, options_);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
if ~DynareOptions.nograph
|
||||
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],DynareOptions.nodisplay,DynareOptions.graph_format);
|
||||
create_TeX_loader(DynareOptions,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],[type ' evaluation of moment restrictions'],'moment_restrictions',type,DynareOptions.figures.textwidth*min(ij/ncol,1))
|
||||
if ~options_.nograph
|
||||
dyn_saveas(h2,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],options_.nodisplay,options_.graph_format);
|
||||
create_TeX_loader(options_,[OutputDirectoryName,filesep,fname_,'_',type,'_moment_restrictions'],[type ' evaluation of moment restrictions'],'moment_restrictions',type,options_.figures.textwidth*min(ij/ncol,1))
|
||||
end
|
||||
|
||||
skipline()
|
||||
|
|
|
@ -1,12 +1,13 @@
|
|||
function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, DynareOptions)
|
||||
%
|
||||
function indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, options_)
|
||||
% indmcf = mcf_analysis(lpmat, ibeha, inobeha, options_mcf, options_)
|
||||
|
||||
% Written by Marco Ratto
|
||||
% Joint Research Centre, The European Commission,
|
||||
% marco.ratto@ec.europa.eu
|
||||
%
|
||||
|
||||
% Copyright © 2014 European Commission
|
||||
% Copyright © 2016-2018 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -28,7 +29,7 @@ pvalue_corr = options_mcf.pvalue_corr;
|
|||
alpha2 = options_mcf.alpha2;
|
||||
param_names = options_mcf.param_names;
|
||||
|
||||
if DynareOptions.TeX
|
||||
if options_.TeX
|
||||
if ~isfield(options_mcf,'param_names_tex')
|
||||
param_names_tex = options_mcf.param_names;
|
||||
else
|
||||
|
@ -60,7 +61,7 @@ if ~isempty(indmcf)
|
|||
data_mat=[dproba(indmcf)' proba(indmcf)'];
|
||||
options_temp.noprint=0;
|
||||
dyntable(options_temp,['Smirnov statistics in driving ', title],headers,labels,data_mat,size(labels,2)+2,16,3);
|
||||
if DynareOptions.TeX
|
||||
if options_.TeX
|
||||
labels_TeX=param_names_tex(indmcf);
|
||||
M_temp.dname=OutputDirectoryName ;
|
||||
M_temp.fname=fname_;
|
||||
|
@ -76,11 +77,11 @@ if length(ibeha)>10 && length(inobeha)>10
|
|||
indcorr = indcorr(~ismember(indcorr(:),indmcf));
|
||||
indmcf = [indmcf(:); indcorr(:)];
|
||||
end
|
||||
if ~isempty(indmcf) && ~DynareOptions.nograph
|
||||
if ~isempty(indmcf) && ~options_.nograph
|
||||
skipline()
|
||||
xx=[];
|
||||
if ~ isempty(xparam1), xx=xparam1(indmcf); end
|
||||
scatter_mcf(lpmat(ibeha,indmcf),lpmat(inobeha,indmcf), param_names_tex(indmcf), ...
|
||||
'.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, DynareOptions, ...
|
||||
'.', [fname_,'_',amcf_name], OutputDirectoryName, amcf_title,xx, options_, ...
|
||||
beha_title, nobeha_title)
|
||||
end
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function indmcf = scatter_analysis(lpmat, xdata, options_scatter, DynareOptions)
|
||||
function indmcf = scatter_analysis(lpmat, xdata, options_scatter, options_)
|
||||
%
|
||||
% Written by Marco Ratto
|
||||
% Joint Research Centre, The European Commission,
|
||||
|
@ -25,7 +25,7 @@ function indmcf = scatter_analysis(lpmat, xdata, options_scatter, DynareOptions)
|
|||
|
||||
param_names = options_scatter.param_names;
|
||||
|
||||
if DynareOptions.TeX
|
||||
if options_.TeX
|
||||
if ~isfield(options_scatter,'param_names_tex')
|
||||
param_names_tex = options_scatter.param_names;
|
||||
else
|
||||
|
@ -42,11 +42,11 @@ if isfield(options_scatter,'xparam1')
|
|||
end
|
||||
OutputDirectoryName = options_scatter.OutputDirectoryName;
|
||||
|
||||
if ~DynareOptions.nograph
|
||||
if ~options_.nograph
|
||||
skipline()
|
||||
xx=[];
|
||||
if ~isempty(xparam1)
|
||||
xx=xparam1;
|
||||
end
|
||||
scatter_plots(lpmat, xdata, param_names, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, DynareOptions)
|
||||
scatter_plots(lpmat, xdata, param_names, '.', [fname_, '_', amcf_name], OutputDirectoryName, amcf_title, xx, options_)
|
||||
end
|
||||
|
|
|
@ -1,4 +1,5 @@
|
|||
function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions, beha_name, non_beha_name)
|
||||
function scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, options_, beha_name, non_beha_name)
|
||||
% scatter_mcf(X,Y,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, options_, beha_name, non_beha_name)
|
||||
%
|
||||
% Written by Marco Ratto
|
||||
% Joint Research Centre, The European Commission,
|
||||
|
@ -84,7 +85,7 @@ figtitle_tex=strrep(figtitle,'_','\_');
|
|||
|
||||
fig_nam_=[fnam];
|
||||
if ~nograph
|
||||
hh_fig=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
|
||||
hh_fig=dyn_figure(options_.nodisplay,'name',figtitle);
|
||||
end
|
||||
|
||||
bf = 0.1;
|
||||
|
@ -166,8 +167,8 @@ if ~isoctave
|
|||
end
|
||||
|
||||
if ~nograph
|
||||
dyn_saveas(hh_fig,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
|
||||
if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
|
||||
dyn_saveas(hh_fig,[dirname,filesep,fig_nam_],options_.nodisplay,options_.graph_format);
|
||||
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
||||
fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
|
||||
fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_mcf.m (Dynare).\n');
|
||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function scatter_plots(X,xp,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, DynareOptions)
|
||||
function scatter_plots(X,xp,vnames,plotsymbol, fnam, dirname, figtitle, xparam1, options_)
|
||||
%
|
||||
% Written by Marco Ratto
|
||||
% Joint Research Centre, The European Commission,
|
||||
|
@ -58,7 +58,7 @@ end
|
|||
if nargin<6 || isempty(dirname)
|
||||
dirname='';
|
||||
nograph=1;
|
||||
DynareOptions.nodisplay=0;
|
||||
options_.nodisplay=0;
|
||||
else
|
||||
nograph=0;
|
||||
end
|
||||
|
@ -73,7 +73,7 @@ figtitle_tex=strrep(figtitle,'_','\_');
|
|||
|
||||
fig_nam_=[fnam];
|
||||
|
||||
hh_fig=dyn_figure(DynareOptions.nodisplay,'name',figtitle);
|
||||
hh_fig=dyn_figure(options_.nodisplay,'name',figtitle);
|
||||
set(hh_fig,'userdata',{X,xp})
|
||||
|
||||
bf = 0.1;
|
||||
|
@ -172,8 +172,8 @@ end
|
|||
% end
|
||||
|
||||
if ~nograph
|
||||
dyn_saveas(hh_fig,[dirname,filesep,fig_nam_],DynareOptions.nodisplay,DynareOptions.graph_format);
|
||||
if DynareOptions.TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
|
||||
dyn_saveas(hh_fig,[dirname,filesep,fig_nam_],options_.nodisplay,options_.graph_format);
|
||||
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
||||
fidTeX = fopen([dirname,'/',fig_nam_ '.tex'],'w');
|
||||
fprintf(fidTeX,'%% TeX eps-loader file generated by scatter_plots.m (Dynare).\n');
|
||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
|
||||
|
|
|
@ -1,10 +1,10 @@
|
|||
function [series, p] = histvalf_initvalf(caller, DynareModel, options)
|
||||
|
||||
function [series, p] = histvalf_initvalf(caller, M_, options)
|
||||
% [series, p] = histvalf_initvalf(caller, M_, options)
|
||||
% Handles options for histval_file and initval_file
|
||||
%
|
||||
% INPUTS
|
||||
% - caller [char] row array, name of calling function
|
||||
% - DynareModel [struct] model description, a.k.a M_
|
||||
% - M_ [struct] model description, a.k.a
|
||||
% - options [struct] options specific to histval_file and initval_file
|
||||
%
|
||||
% OUTPUTS
|
||||
|
@ -79,23 +79,23 @@ end
|
|||
|
||||
% checking that all variable are present
|
||||
error_flag = false;
|
||||
for i = 1:DynareModel.orig_endo_nbr
|
||||
if ~series.exist(DynareModel.endo_names{i})
|
||||
dprintf('%s_FILE: endogenous variable %s is missing', caller, DynareModel.endo_names{i})
|
||||
for i = 1:M_.orig_endo_nbr
|
||||
if ~series.exist(M_.endo_names{i})
|
||||
dprintf('%s_FILE: endogenous variable %s is missing', caller, M_.endo_names{i})
|
||||
error_flag = true;
|
||||
end
|
||||
end
|
||||
|
||||
for i = 1:DynareModel.exo_nbr
|
||||
if ~series.exist(DynareModel.exo_names{i})
|
||||
dprintf('%s_FILE: exogenous variable %s is missing', caller, DynareModel.exo_names{i})
|
||||
for i = 1:M_.exo_nbr
|
||||
if ~series.exist(M_.exo_names{i})
|
||||
dprintf('%s_FILE: exogenous variable %s is missing', caller, M_.exo_names{i})
|
||||
error_flag = true;
|
||||
end
|
||||
end
|
||||
|
||||
for i = 1:DynareModel.exo_det_nbr
|
||||
if ~series.exist(DynareModel.exo_det_names{i})
|
||||
dprintf('%s_FILE: exo_det variable %s is missing', caller, DynareModel.exo_det_names{i})
|
||||
for i = 1:M_.exo_det_nbr
|
||||
if ~series.exist(M_.exo_det_names{i})
|
||||
dprintf('%s_FILE: exo_det variable %s is missing', caller, M_.exo_det_names{i})
|
||||
error_flag = true;
|
||||
end
|
||||
end
|
||||
|
@ -104,8 +104,8 @@ if error_flag
|
|||
error('%s_FILE: some variables are missing', caller)
|
||||
end
|
||||
|
||||
if exist(sprintf('+%s/dynamic_set_auxiliary_series.m', DynareModel.fname), 'file')
|
||||
series = feval(sprintf('%s.dynamic_set_auxiliary_series', DynareModel.fname), series, DynareModel.params);
|
||||
if exist(sprintf('+%s/dynamic_set_auxiliary_series.m', M_.fname), 'file')
|
||||
series = feval(sprintf('%s.dynamic_set_auxiliary_series', M_.fname), series, M_.params);
|
||||
end
|
||||
|
||||
% selecting observations
|
||||
|
@ -129,18 +129,18 @@ if isfield(options, 'first_obs') && ~isempty(options.first_obs)
|
|||
error('%s_FILE: first_obs = %d is larger than the number of observations in the data file (%d)', ...
|
||||
caller, options.first_obs, nobs0)
|
||||
elseif isfield(options, 'first_simulation_period')
|
||||
if options.first_obs == options.first_simulation_period - DynareModel.orig_maximum_lag
|
||||
if options.first_obs == options.first_simulation_period - M_.orig_maximum_lag
|
||||
first_obs = periods(options.first_obs);
|
||||
else
|
||||
error('%s_FILE: first_obs = %d and first_simulation_period = %d have values inconsistent with a maximum lag of %d periods', ...
|
||||
caller, options.first_obs, options.first_simulation_period, DynareModel.orig_maximum_lag)
|
||||
caller, options.first_obs, options.first_simulation_period, M_.orig_maximum_lag)
|
||||
end
|
||||
elseif isfield(options, 'firstsimulationperiod')
|
||||
if periods(options.first_obs) == options.firstsimulationperiod - DynareModel.orig_maximum_lag
|
||||
if periods(options.first_obs) == options.firstsimulationperiod - M_.orig_maximum_lag
|
||||
first_obs = periods(options.first_obs);
|
||||
else
|
||||
error('%s_FILE: first_obs = %d and first_simulation_period = %s have values inconsistent with a maximum lag of %d periods', ...
|
||||
caller, options.first_obs, options.firstsimulationperiod, DynareModel.orig_maximum_lag)
|
||||
caller, options.first_obs, options.firstsimulationperiod, M_.orig_maximum_lag)
|
||||
end
|
||||
else
|
||||
first_obs = periods(options.first_obs);
|
||||
|
@ -150,18 +150,18 @@ end
|
|||
|
||||
if isfield(options, 'firstobs') && ~isempty(options.firstobs)
|
||||
if isfield(options, 'first_simulation_period')
|
||||
if options.firstobs == periods(options.first_simulation_period) - DynareModel.orig_maximum_lag
|
||||
if options.firstobs == periods(options.first_simulation_period) - M_.orig_maximum_lag
|
||||
first_obs = options.firstobs;
|
||||
else
|
||||
error('%s_FILE: first_obs = %s and first_simulation_period = %d have values inconsistent with a maximum lag of %d periods', ...
|
||||
caller, options.firstobs, options.first_simulation_period, DynareModel.orig_maximum_lag)
|
||||
caller, options.firstobs, options.first_simulation_period, M_.orig_maximum_lag)
|
||||
end
|
||||
elseif isfield(options, 'firstsimulationperiod')
|
||||
if options.firstobs == options.firstsimulationperiod - DynareModel.orig_maximum_lag
|
||||
if options.firstobs == options.firstsimulationperiod - M_.orig_maximum_lag
|
||||
first_obs = options.firstobs;
|
||||
else
|
||||
error('%s_FILE: firstobs = %s and first_simulation_period = %s have values inconsistent with a maximum lag of %d periods', ...
|
||||
caller, options.firstobs, options.firstsimulationperiod, DynareModel.orig_maximum_lag)
|
||||
caller, options.firstobs, options.firstsimulationperiod, M_.orig_maximum_lag)
|
||||
end
|
||||
else
|
||||
first_obs = options.firstobs;
|
||||
|
@ -171,18 +171,18 @@ end
|
|||
|
||||
if ~first_obs_ispresent
|
||||
if isfield(options, 'first_simulation_period')
|
||||
if options.first_simulation_period < DynareModel.orig_maximum_lag
|
||||
if options.first_simulation_period < M_.orig_maximum_lag
|
||||
error('%s_FILE: first_simulation_period = %d must be larger than the maximum lag (%d)', ...
|
||||
caller, options.first_simulation_period, DynareModel.orig_maximum_lag)
|
||||
caller, options.first_simulation_period, M_.orig_maximum_lag)
|
||||
elseif options.first_simulation_period > nobs0
|
||||
error('%s_FILE: first_simulations_period = %d is larger than the number of observations in the data file (%d)', ...
|
||||
caller, options.first_obs, nobs0)
|
||||
else
|
||||
first_obs = periods(options.first_simulation_period) - DynareModel.orig_maximum_lag;
|
||||
first_obs = periods(options.first_simulation_period) - M_.orig_maximum_lag;
|
||||
end
|
||||
first_obs_ispresent = true;
|
||||
elseif isfield(options, 'firstsimulationperiod')
|
||||
first_obs = options.firstsimulationperiod - DynareModel.orig_maximum_lag;
|
||||
first_obs = options.firstsimulationperiod - M_.orig_maximum_lag;
|
||||
first_obs_ispresent = true;
|
||||
end
|
||||
end
|
||||
|
@ -237,8 +237,8 @@ end
|
|||
|
||||
|
||||
if exist('lastsimulationperiod', 'var')
|
||||
if lastsimulationperiod<=last_obs-DynareModel.orig_maximum_lead
|
||||
last_obs = lastsimulationperiod+DynareModel.orig_maximum_lead;
|
||||
if lastsimulationperiod<=last_obs-M_.orig_maximum_lead
|
||||
last_obs = lastsimulationperiod+M_.orig_maximum_lead;
|
||||
else
|
||||
error('%s_FILE: LAST_SIMULATION_PERIOD is too large compared to the available data.', caller)
|
||||
end
|
||||
|
@ -247,11 +247,11 @@ end
|
|||
if exist('lastsimulationperiod', 'var') && exist('firstsimulationperiod', 'var')
|
||||
p = lastsimulationperiod-firstsimulationperiod+1;
|
||||
elseif exist('lastsimulationperiod', 'var')
|
||||
p = lastsimulationperiod-(first_obs+DynareModel.orig_maximum_lag)+1;
|
||||
p = lastsimulationperiod-(first_obs+M_.orig_maximum_lag)+1;
|
||||
elseif exist('firstsimulationperiod', 'var')
|
||||
p = (last_obs-DynareModel.orig_maximum_lead)-firstsimulationperiod+1;
|
||||
p = (last_obs-M_.orig_maximum_lead)-firstsimulationperiod+1;
|
||||
else
|
||||
p = (last_obs-DynareModel.orig_maximum_lead)-(first_obs+DynareModel.orig_maximum_lag)+1;
|
||||
p = (last_obs-M_.orig_maximum_lead)-(first_obs+M_.orig_maximum_lag)+1;
|
||||
end
|
||||
|
||||
series = series(first_obs:last_obs);
|
||||
|
|
|
@ -99,10 +99,10 @@ if ~isfield(oo_,'initval_decomposition') || isequal(varlist,0)
|
|||
with_epilogue = options_.initial_condition_decomp.with_epilogue;
|
||||
options_.selected_variables_only = 0; %make sure all variables are stored
|
||||
options_.plot_priors=0;
|
||||
[oo,M,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
|
||||
[oo_local,M,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
|
||||
|
||||
% reduced form
|
||||
dr = oo.dr;
|
||||
dr = oo_local.dr;
|
||||
|
||||
% data reordering
|
||||
order_var = dr.order_var;
|
||||
|
@ -114,7 +114,7 @@ if ~isfield(oo_,'initval_decomposition') || isequal(varlist,0)
|
|||
B = dr.ghu;
|
||||
|
||||
% initialization
|
||||
gend = length(oo.SmoothedShocks.(M_.exo_names{1})); %+options_.forecast;
|
||||
gend = length(oo_local.SmoothedShocks.(M_.exo_names{1})); %+options_.forecast;
|
||||
z = zeros(endo_nbr,endo_nbr+2,gend);
|
||||
z(:,end,:) = Smoothed_Variables_deviation_from_mean;
|
||||
|
||||
|
@ -155,15 +155,15 @@ end
|
|||
if ~isequal(varlist,0)
|
||||
|
||||
% if ~options_.no_graph.shock_decomposition
|
||||
oo=oo_;
|
||||
oo.shock_decomposition = oo_.initval_decomposition;
|
||||
oo_local=oo_;
|
||||
oo_local.shock_decomposition = oo_.initval_decomposition;
|
||||
if ~isempty(init2shocks)
|
||||
init2shocks = M_.init2shocks.(init2shocks);
|
||||
n=size(init2shocks,1);
|
||||
for i=1:n
|
||||
j=strmatch(init2shocks{i}{1},M_.endo_names,'exact');
|
||||
oo.shock_decomposition(:,end-1,:)=oo.shock_decomposition(:,j,:)+oo.shock_decomposition(:,end-1,:);
|
||||
oo.shock_decomposition(:,j,:)=0;
|
||||
oo_local.shock_decomposition(:,end-1,:)=oo_local.shock_decomposition(:,j,:)+oo_local.shock_decomposition(:,end-1,:);
|
||||
oo_local.shock_decomposition(:,j,:)=0;
|
||||
end
|
||||
end
|
||||
M_.exo_names = M_.endo_names;
|
||||
|
@ -173,5 +173,5 @@ if ~isequal(varlist,0)
|
|||
options_.plot_shock_decomp.use_shock_groups = '';
|
||||
options_.plot_shock_decomp.init_cond_decomp = 1; % private flag to plotting utilities
|
||||
|
||||
plot_shock_decomposition(M_,oo,options_,varlist);
|
||||
plot_shock_decomposition(M_,oo_local,options_,varlist);
|
||||
end
|
||||
|
|
|
@ -1,14 +1,14 @@
|
|||
function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,Model)
|
||||
% function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,Model)
|
||||
function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,M_)
|
||||
% function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,M_)
|
||||
%
|
||||
% INPUTS
|
||||
% Q: baseline non-heteroskadastic covariance matrix of shocks
|
||||
% smpl: scalar storing end of sample
|
||||
% Model: structure storing the model information
|
||||
% M_: structure storing the model information
|
||||
% Outputs:
|
||||
% Qvec: [n_exo by n_exo by smpl] array of covariance matrices
|
||||
|
||||
% Copyright © 2020-21 Dynare Team
|
||||
% Copyright © 2020-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -28,31 +28,31 @@ function Qvec=get_Qvec_heteroskedastic_filter(Q,smpl,Model)
|
|||
isqdiag = all(all(abs(Q-diag(diag(Q)))<1e-14)); % ie, the covariance matrix is diagonal...
|
||||
Qvec=repmat(Q,[1 1 smpl+1]);
|
||||
for k=1:smpl
|
||||
inx = ~isnan(Model.heteroskedastic_shocks.Qvalue(:,k));
|
||||
inx = ~isnan(M_.heteroskedastic_shocks.Qvalue(:,k));
|
||||
if any(inx)
|
||||
if isqdiag
|
||||
Qvec(inx,inx,k)=diag(Model.heteroskedastic_shocks.Qvalue(inx,k));
|
||||
Qvec(inx,inx,k)=diag(M_.heteroskedastic_shocks.Qvalue(inx,k));
|
||||
else
|
||||
inx = find(inx);
|
||||
for s=1:length(inx)
|
||||
if Q(inx(s),inx(s))>1.e-14
|
||||
tmpscale = sqrt(Model.heteroskedastic_shocks.Qvalue(inx(s),k)./Q(inx(s),inx(s)));
|
||||
tmpscale = sqrt(M_.heteroskedastic_shocks.Qvalue(inx(s),k)./Q(inx(s),inx(s)));
|
||||
Qvec(inx(s),:,k) = Qvec(inx(s),:,k).*tmpscale;
|
||||
Qvec(:,inx(s),k) = Qvec(:,inx(s),k).*tmpscale;
|
||||
else
|
||||
Qvec(inx(s),inx(s),k)=Model.heteroskedastic_shocks.Qvalue(inx(s),k);
|
||||
Qvec(inx(s),inx(s),k)=M_.heteroskedastic_shocks.Qvalue(inx(s),k);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
inx = ~isnan(Model.heteroskedastic_shocks.Qscale(:,k));
|
||||
inx = ~isnan(M_.heteroskedastic_shocks.Qscale(:,k));
|
||||
if any(inx)
|
||||
if isqdiag
|
||||
Qvec(inx,inx,k)=Qvec(inx,inx,k).*diag(Model.heteroskedastic_shocks.Qscale(inx,k));
|
||||
Qvec(inx,inx,k)=Qvec(inx,inx,k).*diag(M_.heteroskedastic_shocks.Qscale(inx,k));
|
||||
else
|
||||
inx = find(inx);
|
||||
for s=1:length(inx)
|
||||
tmpscale = sqrt(Model.heteroskedastic_shocks.Qscale(inx(s),k));
|
||||
tmpscale = sqrt(M_.heteroskedastic_shocks.Qscale(inx(s),k));
|
||||
Qvec(inx(s),:,k) = Qvec(inx(s),:,k).*tmpscale;
|
||||
Qvec(:,inx(s),k) = Qvec(:,inx(s),k).*tmpscale;
|
||||
end
|
||||
|
|
|
@ -1,6 +1,7 @@
|
|||
function [endo_simul,info] = dyn_lmmcp(M,options,oo)
|
||||
function [endo_simul,info] = dyn_lmmcp(M_,options,oo_)
|
||||
% [endo_simul,info] = dyn_lmmcp(M_,options,oo_)
|
||||
|
||||
% Copyright © 2014 Dynare Team
|
||||
% Copyright © 2014-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -17,13 +18,13 @@ function [endo_simul,info] = dyn_lmmcp(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/>.
|
||||
|
||||
[lb,ub,eq_index] = get_complementarity_conditions(M);
|
||||
[lb,ub,eq_index] = get_complementarity_conditions(M_);
|
||||
|
||||
lead_lag_incidence = M.lead_lag_incidence;
|
||||
lead_lag_incidence = M_.lead_lag_incidence;
|
||||
|
||||
ny = M.endo_nbr;
|
||||
ny = M_.endo_nbr;
|
||||
|
||||
max_lag = M.maximum_endo_lag;
|
||||
max_lag = M_.maximum_endo_lag;
|
||||
|
||||
nyp = nnz(lead_lag_incidence(1,:)) ;
|
||||
iyp = find(lead_lag_incidence(1,:)>0) ;
|
||||
|
@ -42,10 +43,10 @@ stop = 0 ;
|
|||
iz = [1:ny+nyp+nyf];
|
||||
|
||||
periods = options.periods;
|
||||
steady_state = oo.steady_state;
|
||||
params = M.params;
|
||||
endo_simul = oo.endo_simul;
|
||||
exo_simul = oo.exo_simul;
|
||||
steady_state = oo_.steady_state;
|
||||
params = M_.params;
|
||||
endo_simul = oo_.endo_simul;
|
||||
exo_simul = oo_.exo_simul;
|
||||
i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
|
||||
i_cols_A1 = find(lead_lag_incidence(2:3,:)');
|
||||
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
|
||||
|
@ -54,7 +55,7 @@ i_upd = ny+(1:periods*ny);
|
|||
|
||||
x = endo_simul(:);
|
||||
|
||||
model_dynamic = str2func([M.fname,'.dynamic']);
|
||||
model_dynamic = str2func([M_.fname,'.dynamic']);
|
||||
z = x(find(lead_lag_incidence'));
|
||||
[res,A] = model_dynamic(z, exo_simul, params, steady_state,2);
|
||||
nnzA = nnz(A);
|
||||
|
|
|
@ -56,18 +56,18 @@ if ~isfinite(summ)
|
|||
end
|
||||
eq_number_string=[eq_number_string, num2str(j1(end))];
|
||||
var_string=[];
|
||||
Model=evalin('base','M_');
|
||||
M_=evalin('base','M_');
|
||||
for ii=1:length(j2)-1
|
||||
var_string=[var_string, Model.endo_names{j2(ii)}, ', '];
|
||||
var_string=[var_string, M_.endo_names{j2(ii)}, ', '];
|
||||
end
|
||||
var_string=[var_string, Model.endo_names{j2(end)}];
|
||||
var_string=[var_string, M_.endo_names{j2(end)}];
|
||||
fprintf('\nAn infinite element was encountered when trying to solve equation(s) %s \n',eq_number_string)
|
||||
fprintf('with respect to the variable(s): %s.\n',var_string)
|
||||
fprintf('The values of the endogenous variables when the problem was encountered were:\n')
|
||||
label_width=size(char(Model.endo_names),2)+2;
|
||||
label_width=size(char(M_.endo_names),2)+2;
|
||||
label_string=sprintf('%%-%us %%8.4g \\n',label_width);
|
||||
for ii=1:length(xold)
|
||||
fprintf(label_string, Model.endo_names{ii}, xold(ii));
|
||||
fprintf(label_string, M_.endo_names{ii}, xold(ii));
|
||||
end
|
||||
skipline();
|
||||
end
|
||||
|
|
|
@ -1,10 +1,10 @@
|
|||
% [dynpp_derivs, dyn_derivs] = k_order_perturbation(dr,DynareModel,DynareOptions)
|
||||
% [dynpp_derivs, dyn_derivs] = k_order_perturbation(dr,M_,options_)
|
||||
% computes a k-th order perturbation solution
|
||||
%
|
||||
% INPUTS
|
||||
% dr: struct describing the reduced form solution of the model.
|
||||
% DynareModel: struct jobs's parameters
|
||||
% DynareOptions: struct job's options
|
||||
% M_: struct jobs's parameters
|
||||
% options_: struct job's options
|
||||
%
|
||||
% OUTPUTS
|
||||
% dynpp_derivs struct Derivatives of the decision rule in Dynare++ format.
|
||||
|
@ -25,7 +25,7 @@
|
|||
% dynare/mex/sources/k_order_perturbation.cc and it uses code provided by
|
||||
% dynare++
|
||||
|
||||
% Copyright © 2013-2021 Dynare Team
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
|
|
@ -1,17 +1,17 @@
|
|||
function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
|
||||
% function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
|
||||
function oo_ = model_comparison(ModelNames,ModelPriors,oo_,options_,fname)
|
||||
% function oo_ = model_comparison(ModelNames,ModelPriors,oo_,options_,fname)
|
||||
% Conducts Bayesian model comparison. This function computes Odds ratios and
|
||||
% estimates a posterior density over a collection of models.
|
||||
%
|
||||
% INPUTS
|
||||
% ModelNames [string] m*1 cell array of string.
|
||||
% ModelPriors [double] m*1 vector of prior probabilities
|
||||
% oo [struct] Dynare results structure
|
||||
% oo_ [struct] Dynare results structure
|
||||
% options_ [struct] Dynare options structure
|
||||
% fname [string] name of the current mod-file
|
||||
%
|
||||
% OUTPUTS
|
||||
% oo [struct] Dynare results structure containing the
|
||||
% oo_ [struct] Dynare results structure containing the
|
||||
% results in a field PosteriorOddsTable
|
||||
%
|
||||
% ALGORITHM
|
||||
|
@ -20,7 +20,7 @@ function oo = model_comparison(ModelNames,ModelPriors,oo,options_,fname)
|
|||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2007-2018 Dynare Team
|
||||
% Copyright © 2007-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -78,7 +78,7 @@ iname = strmatch(fname,ShortModelNames,'exact');
|
|||
|
||||
for i=1:NumberOfModels
|
||||
if i==iname
|
||||
mstruct.oo_ = oo;
|
||||
mstruct.oo_ = oo_;
|
||||
else
|
||||
if length(ModelNames{i})>3 && (strcmpi(ModelNames{i}(end-3:end),'.mod') || strcmpi(ModelNames{i}(end-3:end),'.dyn'))
|
||||
mstruct = load([ModelNames{i}(1:end-4) filesep 'Output' ModelNames{i}(1:end-4) '_results.mat' ],'oo_');
|
||||
|
@ -127,7 +127,7 @@ end
|
|||
|
||||
for model_iter = 1:NumberOfModels
|
||||
for var_iter = 1:length(labels)
|
||||
oo.Model_Comparison.(headers{1+model_iter}).(field_labels{var_iter}) = values(var_iter, model_iter);
|
||||
oo_.Model_Comparison.(headers{1+model_iter}).(field_labels{var_iter}) = values(var_iter, model_iter);
|
||||
end
|
||||
end
|
||||
|
||||
|
|
|
@ -46,7 +46,7 @@ if isempty(init_flag)
|
|||
init_flag = 1;
|
||||
end
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
% Set local state space model (first order approximation).
|
||||
ghx = ReducedForm.ghx;
|
||||
|
@ -83,7 +83,7 @@ state_variance_rank = size(StateVectorVarianceSquareRoot,2);
|
|||
%end
|
||||
|
||||
% Set seed for randn().
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
|
||||
% Initialization of the likelihood.
|
||||
const_lik = log(2*pi)*number_of_observed_variables;
|
||||
|
|
|
@ -1,9 +1,9 @@
|
|||
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, DynareOptions, Model)
|
||||
|
||||
function [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, options_, M_)
|
||||
% [LIK,lik] = auxiliary_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, options_, M_)
|
||||
% Evaluates the likelihood of a nonlinear model with the auxiliary particle filter
|
||||
% allowing eventually resampling.
|
||||
%
|
||||
% Copyright © 2011-2022 Dynare Team
|
||||
% Copyright © 2011-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare (particles module).
|
||||
%
|
||||
|
@ -25,7 +25,7 @@ if isempty(start)
|
|||
start = 1;
|
||||
end
|
||||
% Get perturbation order
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
% Set flag for prunning
|
||||
pruning = ParticleOptions.pruning;
|
||||
|
@ -77,7 +77,7 @@ state_variance_rank = size(StateVectorVarianceSquareRoot,2);
|
|||
Q_lower_triangular_cholesky = chol(Q)';
|
||||
|
||||
% Set seed for randn().
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
|
||||
% Initialization of the likelihood.
|
||||
const_lik = log(2*pi)*number_of_observed_variables+log(det(H));
|
||||
|
@ -119,7 +119,7 @@ for t=1:sample_size
|
|||
end
|
||||
else
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations,number_of_particles), dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations,number_of_particles), dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
|
||||
|
@ -152,7 +152,7 @@ for t=1:sample_size
|
|||
StateVectors_ = tmp_(mf0_,:);
|
||||
else
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function [ProposalStateVector, Weights, flag] = conditional_filter_proposal(ReducedForm, y, StateVectors, SampleWeights, Q_lower_triangular_cholesky, H_lower_triangular_cholesky, ...
|
||||
H, ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
H, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
|
||||
% Computes the proposal for each past particle using Gaussian approximations
|
||||
% for the state errors and the Kalman filter
|
||||
|
@ -14,8 +14,8 @@ function [ProposalStateVector, Weights, flag] = conditional_filter_proposal(Redu
|
|||
% - H
|
||||
% - ParticleOptions
|
||||
% - ThreadsOptions
|
||||
% - DynareOptions
|
||||
% - Model
|
||||
% - options_
|
||||
% - M_
|
||||
%
|
||||
% OUTPUTS
|
||||
% - ProposalStateVector
|
||||
|
@ -41,7 +41,7 @@ function [ProposalStateVector, Weights, flag] = conditional_filter_proposal(Redu
|
|||
|
||||
flag = false;
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
if ReducedForm.use_k_order_solver
|
||||
dr = ReducedForm.dr;
|
||||
|
@ -93,7 +93,7 @@ epsilon = Q_lower_triangular_cholesky*nodes';
|
|||
yhat = repmat(StateVectors-state_variables_steady_state, 1, size(epsilon, 2));
|
||||
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
|
||||
|
@ -159,6 +159,6 @@ if ParticleOptions.cpf_weights_method.murrayjonesparslow
|
|||
end
|
||||
Prior = probability2(PredictedStateMean, PredictedStateVarianceSquareRoot, ProposalStateVector);
|
||||
Posterior = probability2(StateVectorMean, StateVectorVarianceSquareRoot, ProposalStateVector);
|
||||
Likelihood = probability2(y, H_lower_triangular_cholesky, measurement_equations(ProposalStateVector, ReducedForm, ThreadsOptions, DynareOptions, Model));
|
||||
Likelihood = probability2(y, H_lower_triangular_cholesky, measurement_equations(ProposalStateVector, ReducedForm, ThreadsOptions, options_, M_));
|
||||
Weights = SampleWeights.*Likelihood.*(Prior./Posterior);
|
||||
end
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [LIK,lik] = conditional_particle_filter(ReducedForm, Y, s, ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
function [LIK,lik] = conditional_particle_filter(ReducedForm, Y, s, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
|
||||
% Evaluates the likelihood of a non-linear model with a particle filter
|
||||
%
|
||||
|
@ -8,8 +8,8 @@ function [LIK,lik] = conditional_particle_filter(ReducedForm, Y, s, ParticleOpti
|
|||
% - s [integer] scalar, likelihood evaluation starts at s (has to be smaller than T, the sample length provided in Y).
|
||||
% - ParticlesOptions [struct]
|
||||
% - ThreadsOptions [struct]
|
||||
% - DynareOptions [struct]
|
||||
% - Model [struct]
|
||||
% - options_ [struct]
|
||||
% - M_ [struct]
|
||||
%
|
||||
% OUTPUTS
|
||||
% - LIK [double] scalar, likelihood
|
||||
|
@ -78,7 +78,7 @@ state_variance_rank = size(StateVectorVarianceSquareRoot, 2);
|
|||
Q_lower_triangular_cholesky = chol(Q)';
|
||||
|
||||
% Set seed for randn().
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
|
||||
% Initialization of the likelihood.
|
||||
lik = NaN(T, 1);
|
||||
|
@ -90,7 +90,7 @@ for t=1:T
|
|||
flags = false(n, 1);
|
||||
for i=1:n
|
||||
[StateParticles(:,i), SampleWeights(i), flags(i)] = ...
|
||||
conditional_filter_proposal(ReducedForm, Y(:,t), StateParticles(:,i), SampleWeights(i), Q_lower_triangular_cholesky, H_lower_triangular_cholesky, H, ParticleOptions, ThreadsOptions, DynareOptions, Model);
|
||||
conditional_filter_proposal(ReducedForm, Y(:,t), StateParticles(:,i), SampleWeights(i), Q_lower_triangular_cholesky, H_lower_triangular_cholesky, H, ParticleOptions, ThreadsOptions, options_, M_);
|
||||
end
|
||||
if any(flags)
|
||||
LIK = -Inf;
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sqr_Pss_t_t_1,particles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions,DynareOptions, Model)
|
||||
%
|
||||
function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sqr_Pss_t_t_1,particles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions,options_, M_)
|
||||
% IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sqr_Pss_t_t_1,particles,H,normconst,weigths1,weigths2,ReducedForm,ThreadsOptions,options_, M_)
|
||||
% Elements to calculate the importance sampling ratio
|
||||
%
|
||||
% INPUTS
|
||||
|
@ -20,7 +20,7 @@ function IncrementalWeights = gaussian_densities(obs,mut_t,sqr_Pss_t_t,st_t_1,sq
|
|||
% NOTES
|
||||
% The vector "lik" is used to evaluate the jacobian of the likelihood.
|
||||
|
||||
% Copyright © 2009-2019 Dynare Team
|
||||
% Copyright © 2009-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -44,7 +44,7 @@ proposal = probability2(mut_t, sqr_Pss_t_t, particles);
|
|||
prior = probability2(st_t_1, sqr_Pss_t_t_1, particles);
|
||||
|
||||
% likelihood
|
||||
yt_t_1_i = measurement_equations(particles, ReducedForm, ThreadsOptions, DynareOptions, Model);
|
||||
yt_t_1_i = measurement_equations(particles, ReducedForm, ThreadsOptions, options_, M_);
|
||||
likelihood = probability2(obs, sqrt(H), yt_t_1_i);
|
||||
|
||||
IncrementalWeights = likelihood.*prior./proposal;
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function [LIK,lik] = gaussian_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
|
||||
function [LIK,lik] = gaussian_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
% [LIK,lik] = gaussian_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
% Evaluates the likelihood of a non-linear model approximating the
|
||||
% predictive (prior) and filtered (posterior) densities for state variables
|
||||
% by gaussian distributions.
|
||||
|
@ -74,7 +74,7 @@ else
|
|||
end
|
||||
|
||||
if ParticleOptions.distribution_approximation.montecarlo
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
end
|
||||
|
||||
% Get covariance matrices
|
||||
|
@ -101,20 +101,20 @@ LIK = NaN;
|
|||
for t=1:sample_size
|
||||
[PredictedStateMean, PredictedStateVarianceSquareRoot, StateVectorMean, StateVectorVarianceSquareRoot] = ...
|
||||
gaussian_filter_bank(ReducedForm, Y(:,t), StateVectorMean, StateVectorVarianceSquareRoot, Q_lower_triangular_cholesky, H_lower_triangular_cholesky, ...
|
||||
H, ParticleOptions, ThreadsOptions, DynareOptions, Model);
|
||||
H, ParticleOptions, ThreadsOptions, options_, M_);
|
||||
if ParticleOptions.distribution_approximation.cubature || ParticleOptions.distribution_approximation.unscented
|
||||
StateParticles = bsxfun(@plus, StateVectorMean, StateVectorVarianceSquareRoot*nodes2');
|
||||
IncrementalWeights = gaussian_densities(Y(:,t), StateVectorMean, StateVectorVarianceSquareRoot, PredictedStateMean, ...
|
||||
PredictedStateVarianceSquareRoot, StateParticles, H, const_lik, ...
|
||||
weights2, weights_c2, ReducedForm, ThreadsOptions, ...
|
||||
DynareOptions, Model);
|
||||
options_, M_);
|
||||
SampleWeights = weights2.*IncrementalWeights;
|
||||
else
|
||||
StateParticles = bsxfun(@plus, StateVectorVarianceSquareRoot*randn(state_variance_rank, number_of_particles), StateVectorMean) ;
|
||||
IncrementalWeights = gaussian_densities(Y(:,t), StateVectorMean, StateVectorVarianceSquareRoot, PredictedStateMean, ...
|
||||
PredictedStateVarianceSquareRoot,StateParticles,H,const_lik, ...
|
||||
1/number_of_particles,1/number_of_particles,ReducedForm,ThreadsOptions, ...
|
||||
DynareOptions, Model);
|
||||
options_, M_);
|
||||
SampleWeights = IncrementalWeights/number_of_particles;
|
||||
end
|
||||
SampleWeights = SampleWeights + 1e-6*ones(size(SampleWeights, 1), 1);
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
function [PredictedStateMean, PredictedStateVarianceSquareRoot, StateVectorMean, StateVectorVarianceSquareRoot] = ...
|
||||
gaussian_filter_bank(ReducedForm, obs, StateVectorMean, StateVectorVarianceSquareRoot, Q_lower_triangular_cholesky, H_lower_triangular_cholesky, H, ...
|
||||
ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
ParticleOptions, ThreadsOptions, options_, M_)
|
||||
%
|
||||
% Computes the proposal with a gaussian approximation for importance
|
||||
% sampling
|
||||
|
@ -39,7 +39,7 @@ function [PredictedStateMean, PredictedStateVarianceSquareRoot, StateVectorMean,
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
if ReducedForm.use_k_order_solver
|
||||
dr = ReducedForm.dr;
|
||||
|
@ -95,7 +95,7 @@ StateVectors = sigma_points(1:number_of_state_variables,:);
|
|||
epsilon = sigma_points(number_of_state_variables+1:number_of_state_variables+number_of_structural_innovations,:);
|
||||
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
|
||||
|
|
|
@ -1,6 +1,6 @@
|
|||
function IncrementalWeights = gaussian_mixture_densities(obs, StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
|
||||
StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
|
||||
ReducedForm, ThreadsOptions, DynareOptions, Model)
|
||||
ReducedForm, ThreadsOptions, options_, M_)
|
||||
|
||||
% Elements to calculate the importance sampling ratio
|
||||
%
|
||||
|
@ -22,7 +22,7 @@ function IncrementalWeights = gaussian_mixture_densities(obs, StateMuPrior, Sta
|
|||
% NOTES
|
||||
% The vector "lik" is used to evaluate the jacobian of the likelihood.
|
||||
|
||||
% Copyright © 2009-2019 Dynare Team
|
||||
% Copyright © 2009-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -48,7 +48,7 @@ prior = prior';
|
|||
proposal = proposal';
|
||||
|
||||
% Compute the density of the current observation conditionally to each particle
|
||||
yt_t_1_i = measurement_equations(StateParticles, ReducedForm, ThreadsOptions, DynareOptions, Model);
|
||||
yt_t_1_i = measurement_equations(StateParticles, ReducedForm, ThreadsOptions, options_, M_);
|
||||
|
||||
% likelihood
|
||||
likelihood = probability2(obs, sqrt(H), yt_t_1_i);
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [LIK, lik] = gaussian_mixture_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
function [LIK, lik] = gaussian_mixture_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
|
||||
% Evaluates the likelihood of a non-linear model approximating the state
|
||||
% variables distributions with gaussian mixtures. Gaussian Mixture allows reproducing
|
||||
|
@ -79,7 +79,7 @@ else
|
|||
end
|
||||
|
||||
if ParticleOptions.distribution_approximation.montecarlo
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
end
|
||||
|
||||
% Get covariance matrices
|
||||
|
@ -178,7 +178,7 @@ for t=1:sample_size
|
|||
gaussian_mixture_filter_bank(ReducedForm,Y(:,t), StateMu(:,g), StateSqrtP(:,:,g), StateWeights(g),...
|
||||
StructuralShocksMu(:,i), StructuralShocksSqrtP(:,:,i), StructuralShocksWeights(i),...
|
||||
ObservationShocksWeights(j), H, H_lower_triangular_cholesky, const_lik, ...
|
||||
ParticleOptions, ThreadsOptions, DynareOptions, Model);
|
||||
ParticleOptions, ThreadsOptions, options_, M_);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
@ -192,7 +192,7 @@ for t=1:sample_size
|
|||
StateParticles = bsxfun(@plus, StateMuPost(:,i), StateSqrtPPost(:,:,i)*nodes');
|
||||
IncrementalWeights = gaussian_mixture_densities(Y(:,t), StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
|
||||
StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
|
||||
ReducedForm, ThreadsOptions, DynareOptions, Model);
|
||||
ReducedForm, ThreadsOptions, options_, M_);
|
||||
SampleWeights(i) = sum(StateWeightsPost(i)*weights.*IncrementalWeights);
|
||||
end
|
||||
SumSampleWeights = sum(SampleWeights);
|
||||
|
@ -210,7 +210,7 @@ for t=1:sample_size
|
|||
StateParticles = importance_sampling(StateMuPost,StateSqrtPPost,StateWeightsPost',number_of_particles);
|
||||
IncrementalWeights = gaussian_mixture_densities(Y(:,t), StateMuPrior, StateSqrtPPrior, StateWeightsPrior, ...
|
||||
StateMuPost, StateSqrtPPost, StateWeightsPost, StateParticles, H, ...
|
||||
ReducedForm, ThreadsOptions, DynareOptions, Model);
|
||||
ReducedForm, ThreadsOptions, options_, M_);
|
||||
SampleWeights = IncrementalWeights/number_of_particles;
|
||||
SumSampleWeights = sum(SampleWeights,1);
|
||||
SampleWeights = SampleWeights./SumSampleWeights;
|
||||
|
|
|
@ -2,7 +2,7 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
|
|||
gaussian_mixture_filter_bank(ReducedForm, obs, StateMu, StateSqrtP, StateWeights, ...
|
||||
StructuralShocksMu, StructuralShocksSqrtP, StructuralShocksWeights, ...
|
||||
ObservationShocksWeights, H, H_lower_triangular_cholesky, normfactO, ...
|
||||
ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
ParticleOptions, ThreadsOptions, options_, M_)
|
||||
|
||||
% Computes the proposal with a gaussian approximation for importance
|
||||
% sampling
|
||||
|
@ -41,7 +41,7 @@ function [StateMuPrior,StateSqrtPPrior,StateWeightsPrior,StateMuPost,StateSqrtPP
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
if ReducedForm.use_k_order_solver
|
||||
dr = ReducedForm.dr;
|
||||
|
@ -91,7 +91,7 @@ epsilon = bsxfun(@plus, StructuralShocksSqrtP*nodes3(:,number_of_state_variables
|
|||
StateVectors = bsxfun(@plus, StateSqrtP*nodes3(:,1:number_of_state_variables)', StateMu);
|
||||
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions, DynareOptions, Model)
|
||||
function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions, options_, M_)
|
||||
|
||||
% Copyright © 2013-2022 Dynare Team
|
||||
%
|
||||
|
@ -17,7 +17,7 @@ function measure = measurement_equations(StateVectors,ReducedForm,ThreadsOptions
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
mf1 = ReducedForm.mf1;
|
||||
if ReducedForm.use_k_order_solver
|
||||
dr = ReducedForm.dr;
|
||||
|
@ -44,7 +44,7 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
|
|||
number_of_structural_innovations = length(ReducedForm.Q);
|
||||
yhat = bsxfun(@minus, StateVectors, state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, size(yhat,2)), dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, size(yhat,2)), dr, M_, options_, udr);
|
||||
measure = tmp(mf1,:);
|
||||
else
|
||||
if order == 2
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, DynareOptions, Model)
|
||||
function [LIK,lik] = nonlinear_kalman_filter(ReducedForm, Y, start, ParticleOptions, ThreadsOptions, options_, M_)
|
||||
|
||||
% Evaluates the likelihood of a non-linear model approximating the
|
||||
% predictive (prior) and filtered (posterior) densities for state variables
|
||||
|
@ -54,7 +54,7 @@ if isempty(start)
|
|||
start = 1;
|
||||
end
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
if ReducedForm.use_k_order_solver
|
||||
dr = ReducedForm.dr;
|
||||
|
@ -105,7 +105,7 @@ else
|
|||
end
|
||||
|
||||
if ParticleOptions.distribution_approximation.montecarlo
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
end
|
||||
|
||||
% Get covariance matrices
|
||||
|
@ -130,7 +130,7 @@ for t=1:sample_size
|
|||
epsilon = sigma_points(number_of_state_variables+1:number_of_state_variables+number_of_structural_innovations,:);
|
||||
yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, ThreadsOptions.local_state_space_iteration_2);
|
||||
|
|
|
@ -1,16 +1,16 @@
|
|||
function [pmean, pmode, pmedian, pstdev, p025, p975, covariance] = online_auxiliary_filter(xparam1, DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, DynareResults)
|
||||
|
||||
function [pmean, pmode, pmedian, pstdev, p025, p975, covariance] = online_auxiliary_filter(xparam1, dataset_, options_, M_, estim_params_, bayestopt_, oo_)
|
||||
% [pmean, pmode, pmedian, pstdev, p025, p975, covariance] = online_auxiliary_filter(xparam1, dataset_, options_, M_, estim_params_, bayestopt_, oo_)
|
||||
% Liu & West particle filter = auxiliary particle filter including Liu & West filter on parameters.
|
||||
%
|
||||
% INPUTS
|
||||
% - xparam1 [double] n×1 vector, Initial condition for the estimated parameters.
|
||||
% - DynareDataset [dseries] Sample used for estimation.
|
||||
% - dataset_ [dseries] Sample used for estimation.
|
||||
% - dataset_info [struct] Description of the sample.
|
||||
% - DynareOptions [struct] Option values (options_).
|
||||
% - Model [struct] Description of the model (M_).
|
||||
% - EstimatedParameters [struct] Description of the estimated parameters (estim_params_).
|
||||
% - BayesInfo [struct] Prior definition (bayestopt_).
|
||||
% - DynareResults [struct] Results (oo_).
|
||||
% - options_ [struct] Option values.
|
||||
% - M_ [struct] Description of the model.
|
||||
% - estim_params_ [struct] Description of the estimated parameters.
|
||||
% - bayestopt_ [struct] Prior definition.
|
||||
% - oo_ [struct] Results.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - pmean [double] n×1 vector, mean of the particles at the end of the sample (for the parameters).
|
||||
|
@ -39,26 +39,26 @@ function [pmean, pmode, pmedian, pstdev, p025, p975, covariance] = online_auxili
|
|||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
% Set seed for randn().
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
pruning = DynareOptions.particle.pruning;
|
||||
second_resample = DynareOptions.particle.resampling.status.systematic;
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
pruning = options_.particle.pruning;
|
||||
second_resample = options_.particle.resampling.status.systematic;
|
||||
variance_update = true;
|
||||
|
||||
bounds = prior_bounds(BayesInfo, DynareOptions.prior_trunc); % Reset bounds as lb and ub must only be operational during mode-finding
|
||||
bounds = prior_bounds(bayestopt_, options_.prior_trunc); % Reset bounds as lb and ub must only be operational during mode-finding
|
||||
|
||||
% initialization of state particles
|
||||
[~, Model, DynareOptions, DynareResults, ReducedForm] = solve_model_for_online_filter(true, xparam1, DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, bounds, DynareResults);
|
||||
[~, M_, options_, oo_, ReducedForm] = solve_model_for_online_filter(true, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
mf0 = ReducedForm.mf0;
|
||||
mf1 = ReducedForm.mf1;
|
||||
number_of_particles = DynareOptions.particle.number_of_particles;
|
||||
number_of_particles = options_.particle.number_of_particles;
|
||||
number_of_parameters = size(xparam1,1);
|
||||
Y = DynareDataset.data;
|
||||
Y = dataset_.data;
|
||||
sample_size = size(Y,1);
|
||||
number_of_observed_variables = length(mf1);
|
||||
number_of_structural_innovations = length(ReducedForm.Q);
|
||||
liu_west_delta = DynareOptions.particle.liu_west_delta;
|
||||
liu_west_delta = options_.particle.liu_west_delta;
|
||||
|
||||
% Get initial conditions for the state particles
|
||||
StateVectorMean = ReducedForm.StateVectorMean;
|
||||
|
@ -81,12 +81,12 @@ b_square = 1-small_a*small_a;
|
|||
|
||||
% Initialization of parameter particles
|
||||
xparam = zeros(number_of_parameters,number_of_particles);
|
||||
Prior = dprior(BayesInfo, DynareOptions.prior_trunc);
|
||||
Prior = dprior(bayestopt_, options_.prior_trunc);
|
||||
for i=1:number_of_particles
|
||||
info = 12042009;
|
||||
while info
|
||||
candidate = Prior.draw();
|
||||
[info, Model, DynareOptions, DynareResults] = solve_model_for_online_filter(false, xparam1, DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, bounds, DynareResults);
|
||||
[info, M_, options_, oo_] = solve_model_for_online_filter(false, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
|
||||
if ~info
|
||||
xparam(:,i) = candidate(:);
|
||||
end
|
||||
|
@ -124,8 +124,8 @@ for t=1:sample_size
|
|||
tau_tilde = zeros(1,number_of_particles);
|
||||
for i=1:number_of_particles
|
||||
% model resolution
|
||||
[info, Model, DynareOptions, DynareResults, ReducedForm] = ...
|
||||
solve_model_for_online_filter(false, fore_xparam(:,i), DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, bounds, DynareResults);
|
||||
[info, M_, options_, oo_, ReducedForm] = ...
|
||||
solve_model_for_online_filter(false, fore_xparam(:,i), dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
|
||||
if ~info(1)
|
||||
steadystate = ReducedForm.steadystate;
|
||||
state_variables_steady_state = ReducedForm.state_variables_steady_state;
|
||||
|
@ -167,22 +167,22 @@ for t=1:sample_size
|
|||
% particle likelihood contribution
|
||||
yhat = bsxfun(@minus, StateVectors(:,i), state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, 1), dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, zeros(number_of_structural_innovations, 1), dr, M_, options_, udr);
|
||||
else
|
||||
if pruning
|
||||
yhat_ = bsxfun(@minus,StateVectors_(:,i),state_variables_steady_state_);
|
||||
if order == 2
|
||||
[tmp, ~] = local_state_space_iteration_2(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, constant, ghxx, ghuu, ghxu, yhat_, steadystate, DynareOptions.threads.local_state_space_iteration_2);
|
||||
[tmp, ~] = local_state_space_iteration_2(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, constant, ghxx, ghuu, ghxu, yhat_, steadystate, options_.threads.local_state_space_iteration_2);
|
||||
elseif order == 3
|
||||
[tmp, tmp_] = local_state_space_iteration_3(yhat_, zeros(number_of_structural_innovations, 1), ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, DynareOptions.threads.local_state_space_iteration_3, pruning);
|
||||
[tmp, tmp_] = local_state_space_iteration_3(yhat_, zeros(number_of_structural_innovations, 1), ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, options_.threads.local_state_space_iteration_3, pruning);
|
||||
else
|
||||
error('Pruning is not available for orders > 3');
|
||||
end
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, constant, ghxx, ghuu, ghxu, DynareOptions.threads.local_state_space_iteration_2);
|
||||
tmp = local_state_space_iteration_2(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, constant, ghxx, ghuu, ghxu, options_.threads.local_state_space_iteration_2);
|
||||
elseif order == 3
|
||||
tmp = local_state_space_iteration_3(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, DynareOptions.threads.local_state_space_iteration_3, pruning);
|
||||
tmp = local_state_space_iteration_3(yhat, zeros(number_of_structural_innovations, 1), ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, options_.threads.local_state_space_iteration_3, pruning);
|
||||
else
|
||||
error('Order > 3: use_k_order_solver should be set to true');
|
||||
end
|
||||
|
@ -196,7 +196,7 @@ for t=1:sample_size
|
|||
end
|
||||
% particles selection
|
||||
tau_tilde = tau_tilde/sum(tau_tilde);
|
||||
indx = resample(0, tau_tilde', DynareOptions.particle);
|
||||
indx = resample(0, tau_tilde', options_.particle);
|
||||
StateVectors = StateVectors(:,indx);
|
||||
xparam = fore_xparam(:,indx);
|
||||
if pruning
|
||||
|
@ -208,13 +208,13 @@ for t=1:sample_size
|
|||
for i=1:number_of_particles
|
||||
info = 12042009;
|
||||
counter=0;
|
||||
while info(1) && counter <DynareOptions.particle.liu_west_max_resampling_tries
|
||||
while info(1) && counter <options_.particle.liu_west_max_resampling_tries
|
||||
counter=counter+1;
|
||||
candidate = xparam(:,i) + chol_sigma_bar*randn(number_of_parameters, 1);
|
||||
if all(candidate>=bounds.lb) && all(candidate<=bounds.ub)
|
||||
% model resolution for new parameters particles
|
||||
[info, Model, DynareOptions, DynareResults, ReducedForm] = ...
|
||||
solve_model_for_online_filter(false, candidate, DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, bounds, DynareResults) ;
|
||||
[info, M_, options_, oo_, ReducedForm] = ...
|
||||
solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_) ;
|
||||
if ~info(1)
|
||||
xparam(:,i) = candidate ;
|
||||
steadystate = ReducedForm.steadystate;
|
||||
|
@ -263,23 +263,23 @@ for t=1:sample_size
|
|||
% compute particles likelihood contribution
|
||||
yhat = bsxfun(@minus,StateVectors(:,i), state_variables_steady_state);
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if pruning
|
||||
yhat_ = bsxfun(@minus,StateVectors_(:,i), state_variables_steady_state_);
|
||||
if order == 2
|
||||
[tmp, tmp_] = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, yhat_, steadystate, DynareOptions.threads.local_state_space_iteration_2);
|
||||
[tmp, tmp_] = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, yhat_, steadystate, options_.threads.local_state_space_iteration_2);
|
||||
elseif order == 3
|
||||
[tmp, tmp_] = local_state_space_iteration_3(yhat_, epsilon, ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, DynareOptions.threads.local_state_space_iteration_3, pruning);
|
||||
[tmp, tmp_] = local_state_space_iteration_3(yhat_, epsilon, ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, options_.threads.local_state_space_iteration_3, pruning);
|
||||
else
|
||||
error('Pruning is not available for orders > 3');
|
||||
end
|
||||
StateVectors_(:,i) = tmp_(mf0_,:);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, DynareOptions.threads.local_state_space_iteration_2);
|
||||
tmp = local_state_space_iteration_2(yhat, epsilon, ghx, ghu, constant, ghxx, ghuu, ghxu, options_.threads.local_state_space_iteration_2);
|
||||
elseif order == 3
|
||||
tmp = local_state_space_iteration_3(yhat, epsilon, ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, DynareOptions.threads.local_state_space_iteration_3, pruning);
|
||||
tmp = local_state_space_iteration_3(yhat, epsilon, ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, steadystate, options_.threads.local_state_space_iteration_3, pruning);
|
||||
else
|
||||
error('Order > 3: use_k_order_solver should be set to true');
|
||||
end
|
||||
|
@ -290,8 +290,8 @@ for t=1:sample_size
|
|||
wtilde(i) = w_stage1(i)*exp(-.5*(const_lik+log(det(ReducedForm.H))+sum(PredictionError.*(ReducedForm.H\PredictionError), 1)));
|
||||
end
|
||||
end
|
||||
if counter==DynareOptions.particle.liu_west_max_resampling_tries
|
||||
fprintf('\nLiu & West particle filter: I haven''t been able to solve the model in %u tries.\n',DynareOptions.particle.liu_west_max_resampling_tries)
|
||||
if counter==options_.particle.liu_west_max_resampling_tries
|
||||
fprintf('\nLiu & West particle filter: I haven''t been able to solve the model in %u tries.\n',options_.particle.liu_west_max_resampling_tries)
|
||||
fprintf('Liu & West particle filter: The last error message was: %s\n',get_error_message(info))
|
||||
fprintf('Liu & West particle filter: You can try to increase liu_west_max_resampling_tries, but most\n')
|
||||
fprintf('Liu & West particle filter: likely there is an issue with the model.\n')
|
||||
|
@ -301,14 +301,14 @@ for t=1:sample_size
|
|||
end
|
||||
% normalization
|
||||
weights = wtilde/sum(wtilde);
|
||||
if variance_update && (neff(weights)<DynareOptions.particle.resampling.threshold*sample_size)
|
||||
if variance_update && (neff(weights)<options_.particle.resampling.threshold*sample_size)
|
||||
variance_update = false;
|
||||
end
|
||||
% final resampling (not advised)
|
||||
if second_resample
|
||||
[~, idmode] = max(weights);
|
||||
mode_xparam(:,t) = xparam(:,idmode);
|
||||
indx = resample(0, weights,DynareOptions.particle);
|
||||
indx = resample(0, weights,options_.particle);
|
||||
StateVectors = StateVectors(:,indx) ;
|
||||
if pruning
|
||||
StateVectors_ = StateVectors_(:,indx);
|
||||
|
@ -372,24 +372,24 @@ pmedian = median_xparam(:,sample_size) ;
|
|||
covariance = mat_var_cov;
|
||||
|
||||
%% Plot parameters trajectory
|
||||
TeX = DynareOptions.TeX;
|
||||
TeX = options_.TeX;
|
||||
|
||||
nr = ceil(sqrt(number_of_parameters)) ;
|
||||
nc = floor(sqrt(number_of_parameters));
|
||||
nbplt = 1 ;
|
||||
|
||||
if TeX
|
||||
fidTeX = fopen([Model.fname '_param_traj.tex'],'w');
|
||||
fidTeX = fopen([M_.fname '_param_traj.tex'],'w');
|
||||
fprintf(fidTeX,'%% TeX eps-loader file generated by online_auxiliary_filter.m (Dynare).\n');
|
||||
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
|
||||
fprintf(fidTeX,' \n');
|
||||
end
|
||||
|
||||
for plt = 1:nbplt
|
||||
hh_fig = dyn_figure(DynareOptions.nodisplay,'Name','Parameters Trajectories');
|
||||
hh_fig = dyn_figure(options_.nodisplay,'Name','Parameters Trajectories');
|
||||
for k=1:length(pmean)
|
||||
subplot(nr,nc,k)
|
||||
[name,texname] = get_the_name(k,TeX,Model,EstimatedParameters,DynareOptions);
|
||||
[name,texname] = get_the_name(k,TeX,M_,estim_params_,options_);
|
||||
% Draw the surface for an interval containing 95% of the particles.
|
||||
area(1:sample_size, ub95_xparam(k,:), 'FaceColor', [.9 .9 .9], 'BaseValue', min(lb95_xparam(k,:)));
|
||||
hold on
|
||||
|
@ -405,12 +405,12 @@ for plt = 1:nbplt
|
|||
axis tight
|
||||
drawnow
|
||||
end
|
||||
dyn_saveas(hh_fig, [Model.fname '_param_traj' int2str(plt)], DynareOptions.nodisplay, DynareOptions.graph_format);
|
||||
dyn_saveas(hh_fig, [M_.fname '_param_traj' int2str(plt)], options_.nodisplay, options_.graph_format);
|
||||
if TeX
|
||||
% TeX eps loader file
|
||||
fprintf(fidTeX,'\\begin{figure}[H]\n');
|
||||
fprintf(fidTeX,'\\centering \n');
|
||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_ParamTraj%s}\n',Model.fname,int2str(plt));
|
||||
fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_ParamTraj%s}\n',M_.fname,int2str(plt));
|
||||
fprintf(fidTeX,'\\caption{Parameters trajectories.}');
|
||||
fprintf(fidTeX,'\\label{Fig:ParametersPlots:%s}\n',int2str(plt));
|
||||
fprintf(fidTeX,'\\end{figure}\n');
|
||||
|
@ -423,10 +423,10 @@ 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'; % Gaussian kernel for Fast Fourier Transform approximation.
|
||||
for plt = 1:nbplt
|
||||
hh_fig = dyn_figure(DynareOptions.nodisplay,'Name','Parameters Densities');
|
||||
hh_fig = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
|
||||
for k=1:length(pmean)
|
||||
subplot(nr,nc,k)
|
||||
[name,texname] = get_the_name(k,TeX,Model,EstimatedParameters,DynareOptions);
|
||||
[name,texname] = get_the_name(k,TeX,M_,estim_params_,options_);
|
||||
optimal_bandwidth = mh_optimal_bandwidth(xparam(k,:)',number_of_particles,bandwidth,kernel_function);
|
||||
[density(:,1),density(:,2)] = kernel_density_estimate(xparam(k,:)', number_of_grid_points, ...
|
||||
number_of_particles, optimal_bandwidth, kernel_function);
|
||||
|
@ -441,8 +441,8 @@ for plt = 1:nbplt
|
|||
axis tight
|
||||
drawnow
|
||||
end
|
||||
dyn_saveas(hh_fig,[ Model.fname '_param_density' int2str(plt) ],DynareOptions.nodisplay,DynareOptions.graph_format);
|
||||
if TeX && any(strcmp('eps',cellstr(DynareOptions.graph_format)))
|
||||
dyn_saveas(hh_fig,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
|
||||
if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
|
||||
% TeX eps loader file
|
||||
fprintf(fidTeX, '\\begin{figure}[H]\n');
|
||||
fprintf(fidTeX,'\\centering \n');
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, DynareOptions, Model)
|
||||
function [LIK,lik] = sequential_importance_particle_filter(ReducedForm,Y,start,ParticleOptions,ThreadsOptions, options_, M_)
|
||||
|
||||
% Evaluates the likelihood of a nonlinear model with a particle filter (optionally with resampling).
|
||||
|
||||
|
@ -37,7 +37,7 @@ steadystate = ReducedForm.steadystate;
|
|||
constant = ReducedForm.constant;
|
||||
state_variables_steady_state = ReducedForm.state_variables_steady_state;
|
||||
|
||||
order = DynareOptions.order;
|
||||
order = options_.order;
|
||||
|
||||
% Set persistent variables (if needed).
|
||||
if isempty(init_flag)
|
||||
|
@ -101,7 +101,7 @@ state_variance_rank = size(StateVectorVarianceSquareRoot,2);
|
|||
Q_lower_triangular_cholesky = chol(Q)';
|
||||
|
||||
% Set seed for randn().
|
||||
DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
|
||||
options_=set_dynare_seed_local_options(options_,'default');
|
||||
|
||||
% Initialization of the weights across particles.
|
||||
weights = ones(1,number_of_particles)/number_of_particles ;
|
||||
|
@ -139,7 +139,7 @@ for t=1:sample_size
|
|||
end
|
||||
else
|
||||
if ReducedForm.use_k_order_solver
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, Model, DynareOptions, udr);
|
||||
tmp = local_state_space_iteration_k(yhat, epsilon, dr, M_, options_, udr);
|
||||
else
|
||||
if order == 2
|
||||
tmp = local_state_space_iteration_2(yhat,epsilon,ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
|
||||
|
|
|
@ -1,23 +1,23 @@
|
|||
function [info, Model, DynareOptions, DynareResults, ReducedForm] = ...
|
||||
solve_model_for_online_filter(setinitialcondition, xparam1, DynareDataset, DynareOptions, Model, EstimatedParameters, BayesInfo, bounds, DynareResults)
|
||||
function [info, M_, options_, oo_, ReducedForm] = ...
|
||||
solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
|
||||
|
||||
% Solves the dsge model for an particular parameters set.
|
||||
%
|
||||
% INPUTS
|
||||
% - setinitialcondition [logical] return initial condition if true.
|
||||
% - xparam1 [double] n×1 vector, parameter values.
|
||||
% - DynareDataset [struct] Dataset for estimation (dataset_).
|
||||
% - DynareOptions [struct] Dynare options (options_).
|
||||
% - Model [struct] Model description (M_).
|
||||
% - EstimatedParameters [struct] Estimated parameters (estim_params_).
|
||||
% - BayesInfo [struct] Prior definition (bayestopt_).
|
||||
% - DynareResults [struct] Dynare results (oo_).
|
||||
% - dataset_ [struct] Dataset for estimation.
|
||||
% - options_ [struct] Dynare options.
|
||||
% - M_ [struct] Model description.
|
||||
% - estim_params_ [struct] Estimated parameters.
|
||||
% - bayestopt_ [struct] Prior definition.
|
||||
% - oo_ [struct] Dynare results.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - info [integer] scalar, nonzero if any problem occur when computing the reduced form.
|
||||
% - Model [struct] Model description (M_).
|
||||
% - DynareOptions [struct] Dynare options (options_).
|
||||
% - DynareResults [struct] Dynare results (oo_).
|
||||
% - M_ [struct] M_ description.
|
||||
% - options_ [struct] Dynare options.
|
||||
% - oo_ [struct] Dynare results.
|
||||
% - ReducedForm [struct] Reduced form model.
|
||||
|
||||
% Copyright © 2013-2023 Dynare Team
|
||||
|
@ -58,27 +58,27 @@ if any(xparam1>bounds.ub)
|
|||
end
|
||||
|
||||
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
|
||||
Q = Model.Sigma_e;
|
||||
H = Model.H;
|
||||
for i=1:EstimatedParameters.nvx
|
||||
k =EstimatedParameters.var_exo(i,1);
|
||||
Q = M_.Sigma_e;
|
||||
H = M_.H;
|
||||
for i=1:estim_params_.nvx
|
||||
k =estim_params_.var_exo(i,1);
|
||||
Q(k,k) = xparam1(i)*xparam1(i);
|
||||
end
|
||||
offset = EstimatedParameters.nvx;
|
||||
if EstimatedParameters.nvn
|
||||
for i=1:EstimatedParameters.nvn
|
||||
offset = estim_params_.nvx;
|
||||
if estim_params_.nvn
|
||||
for i=1:estim_params_.nvn
|
||||
H(i,i) = xparam1(i+offset)*xparam1(i+offset);
|
||||
end
|
||||
offset = offset+EstimatedParameters.nvn;
|
||||
offset = offset+estim_params_.nvn;
|
||||
else
|
||||
H = zeros(size(DynareDataset.data, 2));
|
||||
H = zeros(size(dataset_.data, 2));
|
||||
end
|
||||
|
||||
% Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
|
||||
if EstimatedParameters.ncx
|
||||
for i=1:EstimatedParameters.ncx
|
||||
k1 =EstimatedParameters.corrx(i,1);
|
||||
k2 =EstimatedParameters.corrx(i,2);
|
||||
if estim_params_.ncx
|
||||
for i=1:estim_params_.ncx
|
||||
k1 =estim_params_.corrx(i,1);
|
||||
k2 =estim_params_.corrx(i,2);
|
||||
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
||||
Q(k2,k1) = Q(k1,k2);
|
||||
end
|
||||
|
@ -89,13 +89,13 @@ if EstimatedParameters.ncx
|
|||
info = 43;
|
||||
return
|
||||
end
|
||||
offset = offset+EstimatedParameters.ncx;
|
||||
offset = offset+estim_params_.ncx;
|
||||
end
|
||||
|
||||
% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
|
||||
if EstimatedParameters.ncn
|
||||
corrn_observable_correspondence = EstimatedParameters.corrn_observable_correspondence;
|
||||
for i=1:EstimatedParameters.ncn
|
||||
if estim_params_.ncn
|
||||
corrn_observable_correspondence = estim_params_.corrn_observable_correspondence;
|
||||
for i=1:estim_params_.ncn
|
||||
k1 = corrn_observable_correspondence(i,1);
|
||||
k2 = corrn_observable_correspondence(i,2);
|
||||
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
||||
|
@ -108,25 +108,25 @@ if EstimatedParameters.ncn
|
|||
info = 44;
|
||||
return
|
||||
end
|
||||
offset = offset+EstimatedParameters.ncn;
|
||||
offset = offset+estim_params_.ncn;
|
||||
end
|
||||
|
||||
% Update estimated structural parameters in Mode.params.
|
||||
if EstimatedParameters.np > 0
|
||||
Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
|
||||
if estim_params_.np > 0
|
||||
M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||
end
|
||||
|
||||
% Update Model.Sigma_e and Model.H.
|
||||
Model.Sigma_e = Q;
|
||||
Model.H = H;
|
||||
% Update M_.Sigma_e and M_.H.
|
||||
M_.Sigma_e = Q;
|
||||
M_.H = H;
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
% 2. call model setup & reduction program
|
||||
%------------------------------------------------------------------------------
|
||||
|
||||
warning('off', 'MATLAB:nearlySingularMatrix')
|
||||
[~, ~, ~, info, DynareResults.dr, Model.params] = ...
|
||||
dynare_resolve(Model, DynareOptions, DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state, 'restrict');
|
||||
[~, ~, ~, info, oo_.dr, M_.params] = ...
|
||||
dynare_resolve(M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, 'restrict');
|
||||
warning('on', 'MATLAB:nearlySingularMatrix')
|
||||
|
||||
if info(1)~=0
|
||||
|
@ -137,12 +137,12 @@ if info(1)~=0
|
|||
end
|
||||
|
||||
% Get decision rules and transition equations.
|
||||
dr = DynareResults.dr;
|
||||
dr = oo_.dr;
|
||||
|
||||
% Set persistent variables (first call).
|
||||
if isempty(init_flag)
|
||||
mf0 = BayesInfo.mf0;
|
||||
mf1 = BayesInfo.mf1;
|
||||
mf0 = bayestopt_.mf0;
|
||||
mf1 = bayestopt_.mf1;
|
||||
restrict_variables_idx = dr.restrict_var_list;
|
||||
state_variables_idx = restrict_variables_idx(mf0);
|
||||
number_of_state_variables = length(mf0);
|
||||
|
@ -155,17 +155,17 @@ if nargout>4
|
|||
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
|
||||
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
|
||||
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
|
||||
if DynareOptions.order==2
|
||||
if options_.order==2
|
||||
ReducedForm.use_k_order_solver = false;
|
||||
ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
|
||||
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
|
||||
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
|
||||
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
|
||||
ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:);
|
||||
elseif DynareOptions.order>=3
|
||||
elseif options_.order>=3
|
||||
ReducedForm.use_k_order_solver = true;
|
||||
ReducedForm.dr = dr;
|
||||
ReducedForm.udr = folded_to_unfolded_dr(dr, Model, DynareOptions);
|
||||
ReducedForm.udr = folded_to_unfolded_dr(dr, M_, options_);
|
||||
else
|
||||
n_states=size(dr.ghx,2);
|
||||
n_shocks=size(dr.ghu,2);
|
||||
|
@ -184,28 +184,28 @@ end
|
|||
|
||||
% Set initial condition
|
||||
if setinitialcondition
|
||||
switch DynareOptions.particle.initialization
|
||||
switch options_.particle.initialization
|
||||
case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
|
||||
StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
|
||||
[A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns);
|
||||
StateVectorVariance = lyapunov_symm(A, B*ReducedForm.Q*B', DynareOptions.lyapunov_fixed_point_tol, ...
|
||||
DynareOptions.qz_criterium, DynareOptions.lyapunov_complex_threshold, [], DynareOptions.debug);
|
||||
StateVectorVariance = lyapunov_symm(A, B*ReducedForm.Q*B', options_.lyapunov_fixed_point_tol, ...
|
||||
options_.qz_criterium, options_.lyapunov_complex_threshold, [], options_.debug);
|
||||
StateVectorVariance = StateVectorVariance(mf0,mf0);
|
||||
case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
|
||||
StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
|
||||
old_DynareOptionsperiods = DynareOptions.periods;
|
||||
DynareOptions.periods = 5000;
|
||||
old_DynareOptionspruning = DynareOptions.pruning;
|
||||
DynareOptions.pruning = DynareOptions.particle.pruning;
|
||||
y_ = simult(dr.ys, dr, Model, DynareOptions);
|
||||
y_ = y_(dr.order_var(state_variables_idx),2001:DynareOptions.periods);
|
||||
old_DynareOptionsperiods = options_.periods;
|
||||
options_.periods = 5000;
|
||||
old_DynareOptionspruning = options_.pruning;
|
||||
options_.pruning = options_.particle.pruning;
|
||||
y_ = simult(dr.ys, dr, M_, options_);
|
||||
y_ = y_(dr.order_var(state_variables_idx),2001:options_.periods);
|
||||
StateVectorVariance = cov(y_');
|
||||
DynareOptions.periods = old_DynareOptionsperiods;
|
||||
DynareOptions.pruning = old_DynareOptionspruning;
|
||||
options_.periods = old_DynareOptionsperiods;
|
||||
options_.pruning = old_DynareOptionspruning;
|
||||
clear('old_DynareOptionsperiods','y_');
|
||||
case 3% Initial state vector covariance is a diagonal matrix.
|
||||
StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
|
||||
StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
|
||||
StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);
|
||||
otherwise
|
||||
error('Unknown initialization option!')
|
||||
end
|
||||
|
|
|
@ -1,9 +1,9 @@
|
|||
function print_moments_implied_prior(ModelInfo, mm, vm, mv, vv)
|
||||
%function print_moments_implied_prior(ModelInfo, mm, vm, mv, vv)
|
||||
function print_moments_implied_prior(M_, mm, vm, mv, vv)
|
||||
%function print_moments_implied_prior(M_, mm, vm, mv, vv)
|
||||
% This routine prints in the command window some descriptive statistics
|
||||
% about the endogenous variables implied prior moments.
|
||||
% Inputs:
|
||||
% - ModelInfo [structure] Dynare's model structure
|
||||
% - M_ [structure] Dynare's model structure
|
||||
% - mm [endo_nbr*1] mean first moments of the endogenous
|
||||
% variables
|
||||
% - vm [endo_nbr*1] variance of the first moments of the
|
||||
|
@ -14,7 +14,7 @@ function print_moments_implied_prior(ModelInfo, mm, vm, mv, vv)
|
|||
% endogenous variables
|
||||
|
||||
|
||||
% Copyright © 2016-2018 Dynare Team
|
||||
% Copyright © 2016-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -39,14 +39,14 @@ disp(printline(64, '-'))
|
|||
T1 = 'VARIABLE ';
|
||||
T2 = sprintf('Prior mean \t Prior st. dev.');
|
||||
|
||||
for i=1:ModelInfo.orig_endo_nbr
|
||||
Name = ModelInfo.endo_names{i};
|
||||
for i=1:M_.orig_endo_nbr
|
||||
Name = M_.endo_names{i};
|
||||
T1 = strvcat(T1, Name);
|
||||
str = sprintf(' %6.4f \t %6.4f', mm(i), sqrt(vm(i)));
|
||||
T2 = strvcat(T2, str);
|
||||
end
|
||||
|
||||
T0 = repmat(' ', ModelInfo.orig_endo_nbr+1, 1);
|
||||
T0 = repmat(' ', M_.orig_endo_nbr+1, 1);
|
||||
|
||||
TT = [T1, T0, T2];
|
||||
l0 = printline(size(TT, 2)+1, '-');
|
||||
|
@ -64,10 +64,10 @@ T1b = 'VARIABLE-2';
|
|||
T2a = 'Prior mean';
|
||||
T2b = 'Prior st.dev.';
|
||||
|
||||
for i=1:ModelInfo.orig_endo_nbr
|
||||
for j=i:ModelInfo.orig_endo_nbr
|
||||
Name1 = ModelInfo.endo_names{i};
|
||||
Name2 = ModelInfo.endo_names{j};
|
||||
for i=1:M_.orig_endo_nbr
|
||||
for j=i:M_.orig_endo_nbr
|
||||
Name1 = M_.endo_names{i};
|
||||
Name2 = M_.endo_names{j};
|
||||
T1a = strvcat(T1a, Name1);
|
||||
T1b = strvcat(T1b, Name2);
|
||||
sta = sprintf('%12.8f', mv(i,j));
|
||||
|
@ -77,7 +77,7 @@ for i=1:ModelInfo.orig_endo_nbr
|
|||
end
|
||||
end
|
||||
|
||||
T0 = repmat(' ', ModelInfo.orig_endo_nbr*(ModelInfo.orig_endo_nbr+1)/2+1, 1);
|
||||
T0 = repmat(' ', M_.orig_endo_nbr*(M_.orig_endo_nbr+1)/2+1, 1);
|
||||
|
||||
TT = [T1a, T0, T1b, T0, T2a, T0, T2b];
|
||||
l0 = printline(size(TT, 2)+1, '-');
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
function pdraw = prior_draw(BayesInfo, prior_trunc, uniform)
|
||||
|
||||
function pdraw = prior_draw(bayestopt_, prior_trunc, uniform)
|
||||
% pdraw = prior_draw(bayestopt_, prior_trunc, uniform)
|
||||
% This function generate one draw from the joint prior distribution and
|
||||
% allows sampling uniformly from the prior support (uniform==1 when called with init==1)
|
||||
%
|
||||
|
@ -48,18 +48,18 @@ persistent uniform_index gaussian_index gamma_index beta_index inverse_gamma_1_i
|
|||
persistent uniform_draws gaussian_draws gamma_draws beta_draws inverse_gamma_1_draws inverse_gamma_2_draws weibull_draws
|
||||
|
||||
if nargin>0
|
||||
p6 = BayesInfo.p6;
|
||||
p7 = BayesInfo.p7;
|
||||
p3 = BayesInfo.p3;
|
||||
p4 = BayesInfo.p4;
|
||||
bounds = prior_bounds(BayesInfo, prior_trunc);
|
||||
p6 = bayestopt_.p6;
|
||||
p7 = bayestopt_.p7;
|
||||
p3 = bayestopt_.p3;
|
||||
p4 = bayestopt_.p4;
|
||||
bounds = prior_bounds(bayestopt_, prior_trunc);
|
||||
lb = bounds.lb;
|
||||
ub = bounds.ub;
|
||||
number_of_estimated_parameters = length(p6);
|
||||
if nargin>2 && uniform
|
||||
prior_shape = repmat(5,number_of_estimated_parameters,1);
|
||||
else
|
||||
prior_shape = BayesInfo.pshape;
|
||||
prior_shape = bayestopt_.pshape;
|
||||
end
|
||||
beta_index = find(prior_shape==1);
|
||||
if isempty(beta_index)
|
||||
|
@ -238,23 +238,23 @@ for i=1:14
|
|||
end
|
||||
end
|
||||
|
||||
BayesInfo.pshape = p0;
|
||||
BayesInfo.p1 = p1;
|
||||
BayesInfo.p2 = p2;
|
||||
BayesInfo.p3 = p3;
|
||||
BayesInfo.p4 = p4;
|
||||
BayesInfo.p5 = p5;
|
||||
BayesInfo.p6 = p6;
|
||||
BayesInfo.p7 = p7;
|
||||
bayestopt_.pshape = p0;
|
||||
bayestopt_.p1 = p1;
|
||||
bayestopt_.p2 = p2;
|
||||
bayestopt_.p3 = p3;
|
||||
bayestopt_.p4 = p4;
|
||||
bayestopt_.p5 = p5;
|
||||
bayestopt_.p6 = p6;
|
||||
bayestopt_.p7 = p7;
|
||||
|
||||
ndraws = 1e5;
|
||||
m0 = BayesInfo.p1; %zeros(14,1);
|
||||
v0 = diag(BayesInfo.p2.^2); %zeros(14);
|
||||
m0 = bayestopt_.p1; %zeros(14,1);
|
||||
v0 = diag(bayestopt_.p2.^2); %zeros(14);
|
||||
|
||||
% Call the tested routine
|
||||
try
|
||||
% Initialization of prior_draws.
|
||||
prior_draw(BayesInfo, prior_trunc, false);
|
||||
prior_draw(bayestopt_, prior_trunc, false);
|
||||
% Do simulations in a loop and estimate recursively the mean and teh variance.
|
||||
for i = 1:ndraws
|
||||
draw = transpose(prior_draw());
|
||||
|
@ -269,8 +269,8 @@ catch
|
|||
end
|
||||
|
||||
if t(1)
|
||||
t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
|
||||
t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
|
||||
t(2) = all(abs(m0-bayestopt_.p1)<3e-3);
|
||||
t(3) = all(all(abs(v0-diag(bayestopt_.p2.^2))<5e-3));
|
||||
end
|
||||
T = all(t);
|
||||
%@eof:1
|
||||
|
|
|
@ -135,8 +135,8 @@ for j=presample+1:nobs
|
|||
% evalin('base',['options_.nobs=' int2str(j) ';'])
|
||||
options_.nobs=j;
|
||||
if isequal(fast_realtime,0)
|
||||
[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);
|
||||
[oo_local,M_,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
|
||||
gend = size(oo_local.SmoothedShocks.(M_.exo_names{1}),1);
|
||||
else
|
||||
if j<min(fast_realtime) && gend0<j
|
||||
options_.nobs=min(fast_realtime);
|
||||
|
@ -146,10 +146,10 @@ for j=presample+1:nobs
|
|||
end
|
||||
|
||||
if ismember(j,fast_realtime) && gend0<j
|
||||
[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);
|
||||
[oo_local,M_,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
|
||||
gend = size(oo_local.SmoothedShocks.(M_.exo_names{1}),1);
|
||||
gend0 = gend;
|
||||
oo0=oo;
|
||||
oo0=oo_local;
|
||||
Smoothed_Variables_deviation_from_mean0=Smoothed_Variables_deviation_from_mean;
|
||||
else
|
||||
if j>gend0
|
||||
|
@ -164,13 +164,13 @@ for j=presample+1:nobs
|
|||
end
|
||||
|
||||
gend = j;
|
||||
oo=oo0;
|
||||
oo_local=oo0;
|
||||
Smoothed_Variables_deviation_from_mean = Smoothed_Variables_deviation_from_mean0(:,1:gend);
|
||||
end
|
||||
|
||||
end
|
||||
% reduced form
|
||||
dr = oo.dr;
|
||||
dr = oo_local.dr;
|
||||
|
||||
% data reordering
|
||||
order_var = dr.order_var;
|
||||
|
@ -194,7 +194,7 @@ for j=presample+1:nobs
|
|||
% initialization
|
||||
epsilon=NaN(nshocks,gend);
|
||||
for i = 1:nshocks
|
||||
epsilon(i,:) = oo.SmoothedShocks.(M_.exo_names{i})(1:gend);
|
||||
epsilon(i,:) = oo_local.SmoothedShocks.(M_.exo_names{i})(1:gend);
|
||||
end
|
||||
epsilon=[epsilon zeros(nshocks,forecast_)];
|
||||
|
||||
|
|
|
@ -1,6 +1,7 @@
|
|||
function expression = remove_aux_variables_from_expression(expression, DynareModel)
|
||||
function expression = remove_aux_variables_from_expression(expression, M_)
|
||||
% expression = remove_aux_variables_from_expression(expression, M_)
|
||||
|
||||
% Copyright © 2022 Dynare Team
|
||||
% Copyright © 2022-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -19,17 +20,17 @@ function expression = remove_aux_variables_from_expression(expression, DynareMod
|
|||
|
||||
% Get list of endogenous variables in expression
|
||||
list_of_words = regexp(expression, '\<\w*\>', 'match');
|
||||
list_of_words = setdiff(list_of_words, DynareModel.param_names);
|
||||
list_of_words = setdiff(list_of_words, DynareModel.exo_names);
|
||||
list_of_words = setdiff(list_of_words, M_.param_names);
|
||||
list_of_words = setdiff(list_of_words, M_.exo_names);
|
||||
isnotanumber = isnan(str2double(list_of_words));
|
||||
list_of_words = list_of_words(isnotanumber);
|
||||
list_of_words = setdiff(list_of_words, {'diff','log','exp'});
|
||||
|
||||
for i=1:length(list_of_words)
|
||||
id = find(strcmp(list_of_words{i}, DynareModel.endo_names));
|
||||
if isempty(id) || id<=DynareModel.orig_endo_nbr
|
||||
id = find(strcmp(list_of_words{i}, M_.endo_names));
|
||||
if isempty(id) || id<=M_.orig_endo_nbr
|
||||
continue
|
||||
end
|
||||
auxinfo = DynareModel.aux_vars(get_aux_variable_id(id));
|
||||
auxinfo = M_.aux_vars(get_aux_variable_id(id));
|
||||
expression = regexprep(expression, sprintf('\\<%s\\>', list_of_words{i}), auxinfo.orig_expr);
|
||||
end
|
|
@ -1,17 +1,17 @@
|
|||
function DynareModel = set_exogenous_variables_for_simulation(DynareModel)
|
||||
|
||||
function M_ = set_exogenous_variables_for_simulation(M_)
|
||||
% M_ = set_exogenous_variables_for_simulation(M_)
|
||||
% Appends the list of observed exogenous variables in Dynare's model structure (if any).
|
||||
%
|
||||
% INPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
% - M_ [struct] Dynare's model global structure.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
% - M_ [struct] Dynare's model global structure.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2019 Dynare Team
|
||||
% Copyright © 2019-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -28,8 +28,8 @@ function DynareModel = set_exogenous_variables_for_simulation(DynareModel)
|
|||
% 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(DynareModel, 'exo_partitions')
|
||||
if isfield(DynareModel.exo_partitions, 'used')
|
||||
DynareModel.simulation_exo_names = DynareModel.exo_names(~strcmpi('estimationonly', DynareModel.exo_partitions.used));
|
||||
if isfield(M_, 'exo_partitions')
|
||||
if isfield(M_.exo_partitions, 'used')
|
||||
M_.simulation_exo_names = M_.exo_names(~strcmpi('estimationonly', M_.exo_partitions.used));
|
||||
end
|
||||
end
|
|
@ -1,17 +1,17 @@
|
|||
function DynareModel = set_observed_exogenous_variables(DynareModel)
|
||||
|
||||
function M_ = set_observed_exogenous_variables(M_)
|
||||
% M_ = set_observed_exogenous_variables(M_)
|
||||
% Appends the list of observed exogenous variables in Dynare's model structure (if any).
|
||||
%
|
||||
% INPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
% - M_ [struct] Dynare's model global structure.
|
||||
%
|
||||
% OUTPUTS
|
||||
% - DynareModel [struct] Dynare's model global structure, M_.
|
||||
% - M_ [struct] Dynare's model global structure.
|
||||
%
|
||||
% SPECIAL REQUIREMENTS
|
||||
% none
|
||||
|
||||
% Copyright © 2019 Dynare Team
|
||||
% Copyright © 2019-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -28,8 +28,8 @@ function DynareModel = set_observed_exogenous_variables(DynareModel)
|
|||
% 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(DynareModel, 'exo_partitions')
|
||||
if isfield(DynareModel.exo_partitions, 'status')
|
||||
DynareModel.observed_exo_names = DynareModel.exo_names(strcmpi('observed', DynareModel.exo_partitions.status));
|
||||
if isfield(M_, 'exo_partitions')
|
||||
if isfield(M_.exo_partitions, 'status')
|
||||
M_.observed_exo_names = M_.exo_names(strcmpi('observed', M_.exo_partitions.status));
|
||||
end
|
||||
end
|
|
@ -10,8 +10,8 @@ function simulation = simul_static_model(samplesize, innovations)
|
|||
% - simulation [dseries] Simulated endogenous and exogenous variables.
|
||||
%
|
||||
% REMARKS
|
||||
% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
|
||||
% variables are saved in DynareOutput.endo_simul.
|
||||
% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
|
||||
% variables are saved in oo_.endo_simul.
|
||||
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
|
||||
% through the shocks block.
|
||||
|
||||
|
@ -62,7 +62,7 @@ if nargin<2 || isempty(innovations)
|
|||
otherwise
|
||||
error('%s distribution for the structural innovations is not (yet) implemented!', options_.bnlms.innovation_distribution)
|
||||
end
|
||||
% Put the simulated innovations in DynareOutput.exo_simul.
|
||||
% Put the simulated innovations in oo_.exo_simul.
|
||||
oo_.exo_simul = zeros(samplesize, number_of_shocks);
|
||||
oo_.exo_simul(:,positive_var_indx) = oo_.bnlms.shocks;
|
||||
innovations = [];
|
||||
|
|
|
@ -1,8 +1,8 @@
|
|||
function update_all_parameters_in_workspace(DynareModel)
|
||||
|
||||
function update_all_parameters_in_workspace(M_)
|
||||
% update_all_parameters_in_workspace(M_)
|
||||
% Updates all parameter values in Matlab/Octave base workspace.
|
||||
|
||||
% Copyright © 2018 Dynare Team
|
||||
% Copyright © 2018-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -19,6 +19,6 @@ function update_all_parameters_in_workspace(DynareModel)
|
|||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
for i=1:length(DynareModel.params)
|
||||
assignin('base', DynareModel.param_names{i}, DynareModel.params(i));
|
||||
for i=1:length(M_.params)
|
||||
assignin('base', M_.param_names{i}, M_.params(i));
|
||||
end
|
|
@ -1,6 +1,7 @@
|
|||
function ipnames = get_estimated_parameters_indices(params, pnames, eqname, DynareModel)
|
||||
function ipnames = get_estimated_parameters_indices(params, pnames, eqname, M_)
|
||||
% ipnames = get_estimated_parameters_indices(params, pnames, eqname, M_)
|
||||
|
||||
% Copyright © 2021 Dynare Team
|
||||
% Copyright © 2021-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -36,5 +37,5 @@ end
|
|||
|
||||
ipnames = zeros(size(list_of_parameters));
|
||||
for i=1:length(ipnames)
|
||||
ipnames(i) = find(strcmp(list_of_parameters{i}, DynareModel.param_names));
|
||||
ipnames(i) = find(strcmp(list_of_parameters{i}, M_.param_names));
|
||||
end
|
|
@ -36,7 +36,7 @@ objTypes = objTypes(I);
|
|||
for i=1:length(objNames)
|
||||
switch objTypes(i)
|
||||
case 1
|
||||
expression = strrep(expression, objNames{i}, sprintf('DynareModel.params(%u)', objIndex(i)));
|
||||
expression = strrep(expression, objNames{i}, sprintf('M_.params(%u)', objIndex(i)));
|
||||
case 2
|
||||
k = find(strcmp(objNames{i}, data.name));
|
||||
if isempty(k)
|
||||
|
|
|
@ -1,8 +1,8 @@
|
|||
function write_residuals_routine(lhs, rhs, eqname, ipnames, DynareModel, pacmodl)
|
||||
|
||||
function write_residuals_routine(lhs, rhs, eqname, ipnames, M_, pacmodl)
|
||||
% write_residuals_routine(lhs, rhs, eqname, ipnames, M_, pacmodl)
|
||||
% Creates a routine for evaluating the residuals of the nonlinear equation.
|
||||
|
||||
% Copyright © 2021 Dynare Team
|
||||
% Copyright © 2021-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -20,18 +20,18 @@ function write_residuals_routine(lhs, rhs, eqname, ipnames, DynareModel, pacmodl
|
|||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
fun = sprintf('r_%s', eqname);
|
||||
fid = fopen(['+' DynareModel.fname filesep() fun '.m'], 'w');
|
||||
fprintf(fid, 'function r = %s(params, data, DynareModel, DynareOutput)\n', fun);
|
||||
fid = fopen(['+' M_.fname filesep() fun '.m'], 'w');
|
||||
fprintf(fid, 'function r = %s(params, data, M_, oo_)\n', fun);
|
||||
fprintf(fid, '\n');
|
||||
fprintf(fid, '%% Evaluates the residuals for equation %s.\n', eqname);
|
||||
fprintf(fid, '%% File created by Dynare (%s).\n', datetime);
|
||||
fprintf(fid, '\n');
|
||||
for i=1:length(ipnames)
|
||||
fprintf(fid, 'DynareModel.params(%u) = params(%u);\n', ipnames(i), i);
|
||||
fprintf(fid, 'M_.params(%u) = params(%u);\n', ipnames(i), i);
|
||||
end
|
||||
fprintf(fid, '\n');
|
||||
if nargin>5
|
||||
fprintf(fid, 'DynareModel = pac.update.parameters(''%s'', DynareModel, DynareOutput, false);\n', pacmodl);
|
||||
fprintf(fid, 'M_ = pac.update.parameters(''%s'', M_, oo_, false);\n', pacmodl);
|
||||
fprintf(fid, '\n');
|
||||
end
|
||||
fprintf(fid, 'r = %s-(%s);\n', lhs, rhs);
|
||||
|
|
|
@ -1,8 +1,8 @@
|
|||
function write_ssr_routine(lhs, rhs, eqname, ipnames, DynareModel, pacmodl)
|
||||
|
||||
function write_ssr_routine(lhs, rhs, eqname, ipnames, M_, pacmodl)
|
||||
% write_ssr_routine(lhs, rhs, eqname, ipnames, M_, pacmodl)
|
||||
% Creates a routine for evaluating the sum of squared residuals of the nonlinear equation.
|
||||
|
||||
% Copyright © 2021 Dynare Team
|
||||
% Copyright © 2021-2023 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
|
@ -20,8 +20,8 @@ function write_ssr_routine(lhs, rhs, eqname, ipnames, DynareModel, pacmodl)
|
|||
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
fun = sprintf('ssr_%s', eqname);
|
||||
fid = fopen(['+' DynareModel.fname filesep() fun '.m'], 'w');
|
||||
fprintf(fid, 'function [s, fake1, fake2, fake3, fake4] = %s(params, data, DynareModel, DynareOutput)\n', fun);
|
||||
fid = fopen(['+' M_.fname filesep() fun '.m'], 'w');
|
||||
fprintf(fid, 'function [s, fake1, fake2, fake3, fake4] = %s(params, data, M_, oo_)\n', fun);
|
||||
fprintf(fid, '\n');
|
||||
fprintf(fid, '%% Evaluates the sum of square residuals for equation %s.\n', eqname);
|
||||
fprintf(fid, '%% File created by Dynare (%s).\n', datetime);
|
||||
|
@ -32,11 +32,11 @@ fprintf(fid, 'fake3 = [];\n');
|
|||
fprintf(fid, 'fake4 = [];\n');
|
||||
fprintf(fid, '\n');
|
||||
for i=1:length(ipnames)
|
||||
fprintf(fid, 'DynareModel.params(%u) = params(%u);\n', ipnames(i), i);
|
||||
fprintf(fid, 'M_.params(%u) = params(%u);\n', ipnames(i), i);
|
||||
end
|
||||
fprintf(fid, '\n');
|
||||
if nargin>5
|
||||
fprintf(fid, 'DynareModel = pac.update.parameters(''%s'', DynareModel, DynareOutput, false);\n', pacmodl);
|
||||
fprintf(fid, 'M_ = pac.update.parameters(''%s'', M_, oo_, false);\n', pacmodl);
|
||||
fprintf(fid, '\n');
|
||||
end
|
||||
fprintf(fid, 'r = %s-(%s);\n', lhs, rhs);
|
||||
|
|
|
@ -45,7 +45,7 @@ if (size(estim_params_.var_endo,1) || size(estim_params_.corrn,1))
|
|||
end
|
||||
|
||||
% Fill or update bayestopt_ structure
|
||||
[xparam1, EstimatedParameters, BayesOptions, lb, ub, Model] = set_prior(estim_params_, M_, options_);
|
||||
[xparam1, estim_params_, BayesOptions, lb, ub, M_] = set_prior(estim_params_, M_, options_);
|
||||
|
||||
% Get untruncated bounds
|
||||
bounds = prior_bounds(BayesOptions, options_.prior_trunc);
|
||||
|
@ -57,7 +57,7 @@ PriorNames = { 'Beta' , 'Gamma' , 'Gaussian' , 'Inv. Gamma' , 'Uniform' , 'Inv.
|
|||
if ~exist([M_.dname '/latex'],'dir')
|
||||
mkdir(M_.dname,'latex');
|
||||
end
|
||||
fidTeX = fopen([M_.dname, '/latex/' Model.fname '_priors_table.tex'],'w+');
|
||||
fidTeX = fopen([M_.dname, '/latex/' M_.fname '_priors_table.tex'],'w+');
|
||||
fprintf(fidTeX,'%% TeX-table generated by Dynare write_latex_prior_table.m.\n');
|
||||
fprintf(fidTeX,'%% Prior Information\n');
|
||||
fprintf(fidTeX,['%% ' datestr(now,0)]);
|
||||
|
@ -112,7 +112,7 @@ fprintf(fidTeX,'\\endlastfoot\n');
|
|||
% Column 8: the upper bound of the interval containing 90% of the prior mass.
|
||||
PriorIntervals = prior_bounds(BayesOptions,(1-options_.prior_interval)/2) ;
|
||||
for i=1:size(BayesOptions.name,1)
|
||||
[tmp,TexName] = get_the_name(i, 1, Model, EstimatedParameters, options_);
|
||||
[tmp,TexName] = get_the_name(i, 1, M_, estim_params_, options_);
|
||||
PriorShape = PriorNames{ BayesOptions.pshape(i) };
|
||||
PriorMean = BayesOptions.p1(i);
|
||||
PriorMode = BayesOptions.p5(i);
|
||||
|
|
Loading…
Reference in New Issue