1function [dr, info, M, options, oo] = resol(check_flag, M, options, oo) 2 3% Computes the perturbation based decision rules of the DSGE model (orders 1 to 3) 4% 5% INPUTS 6% - check_flag [integer] scalar, equal to 0 if all the approximation is required, equal to 1 if only the eigenvalues are to be computed. 7% - M [structure] Matlab's structure describing the model (M_). 8% - options [structure] Matlab's structure describing the current options (options_). 9% - oo [structure] Matlab's structure containing the results (oo_). 10% 11% OUTPUTS 12% - dr [structure] Reduced form model. 13% - info [integer] scalar or vector, error code. 14% - M [structure] Matlab's structure describing the model (M_). 15% - options [structure] Matlab's structure describing the current options (options_). 16% - oo [structure] Matlab's structure containing the results (oo_). 17% 18% REMARKS 19% Possible values for the error codes are: 20% 21% info(1)=0 -> No error. 22% info(1)=1 -> The model doesn't determine the current variables uniquely. 23% info(1)=2 -> MJDGGES returned an error code. 24% info(1)=3 -> Blanchard & Kahn conditions are not satisfied: no stable equilibrium. 25% info(1)=4 -> Blanchard & Kahn conditions are not satisfied: indeterminacy. 26% info(1)=5 -> Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure. 27% info(1)=6 -> The jacobian evaluated at the deterministic steady state is complex. 28% info(1)=19 -> The steadystate routine has thrown an exception (inconsistent deep parameters). 29% info(1)=20 -> Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations). 30% info(1)=21 -> The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state. 31% info(1)=22 -> The steady has NaNs. 32% info(1)=23 -> M_.params has been updated in the steadystate routine and has complex valued scalars. 33% info(1)=24 -> M_.params has been updated in the steadystate routine and has some NaNs. 34% info(1)=30 -> Ergodic variance can't be computed. 35 36% Copyright (C) 2001-2018 Dynare Team 37% 38% This file is part of Dynare. 39% 40% Dynare is free software: you can redistribute it and/or modify 41% it under the terms of the GNU General Public License as published by 42% the Free Software Foundation, either version 3 of the License, or 43% (at your option) any later version. 44% 45% Dynare is distributed in the hope that it will be useful, 46% but WITHOUT ANY WARRANTY; without even the implied warranty of 47% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 48% GNU General Public License for more details. 49% 50% You should have received a copy of the GNU General Public License 51% along with Dynare. If not, see <http://www.gnu.org/licenses/>. 52 53if isfield(oo,'dr') 54 if isfield(oo.dr,'kstate') 55 dr.kstate = oo.dr.kstate; 56 end 57 if isfield(oo.dr,'inv_order_var') 58 dr.inv_order_var = oo.dr.inv_order_var; 59 end 60 if isfield(oo.dr,'order_var') 61 dr.order_var = oo.dr.order_var; 62 end 63 if isfield(oo.dr,'restrict_var_list') 64 dr.restrict_var_list = oo.dr.restrict_var_list; 65 end 66 if isfield(oo.dr,'restrict_columns') 67 dr.restrict_columns = oo.dr.restrict_columns; 68 end 69 if isfield(oo.dr,'obs_var') 70 dr.obs_var = oo.dr.obs_var; 71 end 72end 73 74if M.exo_nbr == 0 75 oo.exo_steady_state = [] ; 76end 77 78[dr.ys,M.params,info] = evaluate_steady_state(oo.steady_state,M,options,oo,~options.steadystate.nocheck); 79 80if info(1) 81 oo.dr = dr; 82 return 83end 84 85if options.loglinear 86 threshold = 1e-16; 87 % Find variables with non positive steady state. Skip auxiliary 88 % variables for lagges/leaded exogenous variables 89 idx = find(dr.ys(get_all_variables_but_lagged_leaded_exogenous(M))<threshold); 90 if length(idx) 91 if options.debug 92 variables_with_non_positive_steady_state = M.endo_names{idx}; 93 skipline() 94 fprintf('You are attempting to simulate/estimate a loglinear approximation of a model, but\n') 95 fprintf('the steady state level of the following variables is not strictly positive:\n') 96 for var_iter=1:length(idx) 97 fprintf(' - %s (%s)\n',deblank(variables_with_non_positive_steady_state(var_iter,:)), num2str(dr.ys(idx(var_iter)))) 98 end 99 if isinestimationobjective() 100 fprintf('You should check that the priors and/or bounds over the deep parameters are such\n') 101 fprintf('that the steady state levels of all the variables are strictly positive, or consider\n') 102 fprintf('a linearization of the model instead of a log linearization.\n') 103 else 104 fprintf('You should check that the calibration of the deep parameters is such that the\n') 105 fprintf('steady state levels of all the variables are strictly positive, or consider\n') 106 fprintf('a linearization of the model instead of a log linearization.\n') 107 end 108 end 109 info(1)=26; 110 info(2)=sum(dr.ys(dr.ys<threshold).^2); 111 return 112 end 113end 114 115if options.block 116 [dr,info,M,options,oo] = dr_block(dr,check_flag,M,options,oo); 117 dr.edim = nnz(abs(dr.eigval) > options.qz_criterium); 118 dr.sdim = dr.nd-dr.edim; 119else 120 [dr,info] = stochastic_solvers(dr,check_flag,M,options,oo); 121end 122oo.dr = dr; 123