106 lines
4.9 KiB
Matlab
106 lines
4.9 KiB
Matlab
function [dr, info, M, options, oo] = resol(check_flag, M, options, oo)
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% Computes the perturbation based decision rules of the DSGE model (orders 1 to 3)
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%
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% INPUTS
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% - check_flag [integer] scalar, equal to 0 if all the approximation is required, equal to 1 if only the eigenvalues are to be computed.
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% - M [structure] Matlab's structure describing the model (M_).
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% - options [structure] Matlab's structure describing the current options (options_).
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% - oo [structure] Matlab's structure containing the results (oo_).
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%
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% OUTPUTS
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% - dr [structure] Reduced form model.
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% - info [integer] scalar or vector, error code.
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% - M [structure] Matlab's structure describing the model (M_).
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% - options [structure] Matlab's structure describing the current options (options_).
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% - oo [structure] Matlab's structure containing the results (oo_).
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%
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% REMARKS
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% Possible values for the error codes are:
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%
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% info(1)=0 -> No error.
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% info(1)=1 -> The model doesn't determine the current variables uniquely.
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% info(1)=2 -> MJDGGES returned an error code.
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% info(1)=3 -> Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
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% info(1)=4 -> Blanchard & Kahn conditions are not satisfied: indeterminacy.
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% info(1)=5 -> Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
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% info(1)=6 -> The jacobian evaluated at the deterministic steady state is complex.
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% info(1)=19 -> The steadystate routine has thrown an exception (inconsistent deep parameters).
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% info(1)=20 -> Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
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% info(1)=21 -> The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
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% info(1)=22 -> The steady has NaNs.
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% info(1)=23 -> M_.params has been updated in the steadystate routine and has complex valued scalars.
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% info(1)=24 -> M_.params has been updated in the steadystate routine and has some NaNs.
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% info(1)=30 -> Ergodic variance can't be computed.
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% Copyright (C) 2001-2018 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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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 <http://www.gnu.org/licenses/>.
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if isfield(oo,'dr')
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dr = oo.dr;
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end
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if M.exo_nbr == 0
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oo.exo_steady_state = [] ;
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end
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[dr.ys,M.params,info] = evaluate_steady_state(oo.steady_state,M,options,oo,~options.steadystate.nocheck);
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if info(1)
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oo.dr = dr;
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return
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end
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if options.loglinear
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threshold = 1e-16;
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% Find variables with non positive steady state. Skip auxiliary
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% variables for lagges/leaded exogenous variables
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idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M))<threshold);
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if length(idx)
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if options.debug
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variables_with_non_positive_steady_state = M.endo_names{idx};
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skipline()
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fprintf('You are attempting to simulate/estimate a loglinear approximation of a model, but\n')
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fprintf('the steady state level of the following variables is not strictly positive:\n')
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for var_iter=1:length(idx)
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fprintf(' - %s (%s)\n',deblank(variables_with_non_positive_steady_state(var_iter,:)), num2str(dr.ys(idx(var_iter))))
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end
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if isinestimationobjective()
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fprintf('You should check that the priors and/or bounds over the deep parameters are such\n')
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fprintf('that the steady state levels of all the variables are strictly positive, or consider\n')
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fprintf('a linearization of the model instead of a log linearization.\n')
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else
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fprintf('You should check that the calibration of the deep parameters is such that the\n')
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fprintf('steady state levels of all the variables are strictly positive, or consider\n')
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fprintf('a linearization of the model instead of a log linearization.\n')
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end
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end
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info(1)=26;
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info(2)=sum(dr.ys(dr.ys<threshold).^2);
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return
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end
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end
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if options.block
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[dr,info,M,options,oo] = dr_block(dr,check_flag,M,options,oo);
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dr.edim = nnz(abs(dr.eigval) > options.qz_criterium);
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dr.sdim = dr.nd-dr.edim;
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else
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[dr,info] = stochastic_solvers(dr,check_flag,M,options,oo);
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end
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oo.dr = dr;
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