73 lines
3.1 KiB
Matlab
73 lines
3.1 KiB
Matlab
function fjac = fjaco(f,x,varargin)
|
|
% fjac = fjaco(f,x,varargin)
|
|
% FJACO Computes two-sided finite difference Jacobian
|
|
% USAGE
|
|
% fjac = fjaco(f,x,P1,P2,...)
|
|
% INPUTS
|
|
% f : name of function of form fval = f(x)
|
|
% x : evaluation point
|
|
% P1,P2,... : additional arguments for f (optional)
|
|
% OUTPUT
|
|
% fjac : finite difference Jacobian
|
|
%
|
|
% Copyright © 2010-2017,2019-2023 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/>.
|
|
|
|
ff=feval(f,x,varargin{:});
|
|
|
|
tol = eps.^(1/3); %some default value
|
|
if strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective')
|
|
tol= varargin{4}.dynatol.x;
|
|
end
|
|
h = tol.*max(abs(x),1);
|
|
xh1=x+h; xh0=x-h;
|
|
h=xh1-xh0;
|
|
fjac = NaN(length(ff),length(x));
|
|
for j=1:length(x)
|
|
xx = x;
|
|
xx(j) = xh1(j); f1=feval(f,xx,varargin{:});
|
|
if isempty(f1) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
|
|
[~,info]=feval(f,xx,varargin{:});
|
|
disp_info_error_identification_perturbation(info,j);
|
|
end
|
|
xx(j) = xh0(j); f0=feval(f,xx,varargin{:});
|
|
if isempty(f0) && (strcmp(func2str(f),'identification.get_perturbation_params_derivs_numerical_objective') || strcmp(func2str(f),'identification.numerical_objective') )
|
|
[~,info]=feval(f,xx,varargin{:});
|
|
disp_info_error_identification_perturbation(info,j)
|
|
end
|
|
fjac(:,j) = (f1-f0)/h(j);
|
|
end
|
|
|
|
feval(f,x,varargin{:});
|
|
|
|
%Auxiliary functions
|
|
function disp_info_error_identification_perturbation(info,j)
|
|
% there are errors in the solution algorithm
|
|
probl_par = get_the_name(j,varargin{4}.TeX,varargin{3},varargin{2},varargin{4}.varobs);
|
|
skipline()
|
|
message = get_error_message(info,varargin{4});
|
|
fprintf('Parameter error in numerical two-sided difference method:\n')
|
|
fprintf('Cannot solve the model for %s (info = %d, %s)\n', probl_par, info(1), message);
|
|
fprintf('Possible solutions:\n')
|
|
fprintf(' -- check your mod file, calibration and steady state computations carefully\n');
|
|
fprintf(' -- use analytic derivatives, i.e. set analytic_derivation_mode=0\n');
|
|
fprintf(' -- use an estimated_params block without %s or change its value\n', probl_par);
|
|
fprintf(' -- change numerical tolerance level in fjaco.m (you can tune ''options_.dynatol.x'' or change fjaco.m function directly)\n');
|
|
error('fjaco.m: numerical two-sided difference method yields errors in solution algorithm');
|
|
end
|
|
|
|
end %main function end |