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

Ref. !2067
bgp-dev
Sébastien Villemot 2022-09-12 16:58:05 +02:00
parent 90ee301824 85a894efde
commit b071e85214
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GPG Key ID: 2CECE9350ECEBE4A
2 changed files with 13 additions and 5 deletions
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4
matlab/perfect-foresight-models/perfect_foresight_solver_core.m
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@ -116,7 +116,7 @@ else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
solve_stacked_linear_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
else
[oo_.endo_simul, oo_.deterministic_simulation] = ...
[oo_.endo_simul, oo_.deterministic_simulation, residuals] = ...
solve_stacked_problem(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, M_, options_);
end
otherwise
@ -128,7 +128,7 @@ end
if nargout>1
if options_.lmmcp.status
maxerror = NaN; % Could be improved
maxerror = max(max(abs(residuals)));
elseif options_.block && ~options_.bytecode
maxerror = oo_.deterministic_simulation.error;
else

14
matlab/perfect-foresight-models/solve_stacked_problem.m
View File

@ -1,4 +1,4 @@
function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
function [endogenousvariables, info, residuals] = solve_stacked_problem(endogenousvariables, exogenousvariables, steadystate, M, options)
% Solves the perfect foresight model using dynare_solve
%
@ -12,6 +12,7 @@ function [endogenousvariables, info] = solve_stacked_problem(endogenousvariables
% OUTPUTS
% - endogenousvariables [double] N*T array, paths for the endogenous variables (solution of the perfect foresight model).
% - info [struct] contains informations about the results.
% - residuals [double] N*T array, residuals of the equations (with 0 for initial condition)
% Copyright © 2015-2022 Dynare Team
%
@ -46,7 +47,7 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
options.mcppath.lb = repmat(lb,options.periods,1);
options.mcppath.ub = repmat(ub,options.periods,1);
end
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_mcp_problem, z(:), ...
[y, check, res, ~, errorcode] = dynare_solve(@perfect_foresight_mcp_problem, z(:), ...
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
options, ...
dynamicmodel, y0, yT, ...
@ -54,8 +55,10 @@ if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity
M.maximum_lag, options.periods, M.endo_nbr, i_cols, ...
i_cols_J1, i_cols_1, i_cols_T, i_cols_j, i_cols_0, i_cols_J0, ...
eq_index);
eq_to_ignore=find(isfinite(lb) | isfinite(ub));
else
[y, check, ~, ~, errorcode] = dynare_solve(@perfect_foresight_problem, z(:), ...
[y, check, res, ~, errorcode] = dynare_solve(@perfect_foresight_problem, z(:), ...
options.simul.maxit, options.dynatol.f, options.dynatol.x, ...
options, y0, yT, exogenousvariables, M.params, steadystate, options.periods, M, options);
end
@ -69,6 +72,11 @@ else
end
endogenousvariables(:, M.maximum_lag+(1:options.periods)) = reshape(y, M.endo_nbr, options.periods);
residuals=zeros(size(endogenousvariables));
residuals(:, M.maximum_lag+(1:options.periods)) = reshape(res, M.endo_nbr, options.periods);
if (options.solve_algo == 10 || options.solve_algo == 11)% mixed complementarity problem
residuals(eq_to_ignore,endogenousvariables(eq_to_ignore,:)<=lb(eq_to_ignore)+eps | endogenousvariables(eq_to_ignore,:)>=ub(eq_to_ignore)-eps)=0;
end
if check
info.status = false;