dynare/matlab/+occbin/solve_one_constraint.m

function [data, SS_out, error_flag ] = solve_one_constraint(M_,dr, opts_simul_, solve_DM)
%function [data, SS_out, error_flag ] = solve_one_constraint(M_,dr, opts_simul_, solve_DM)
%
% INPUT: 
% - M_                  [structure]     Matlab's structure describing the model (M_).
% - dr                  [structure]     decision rules for the model
% - opts_simul          [structure]     Matlab's structure containing the Occbin options (opts_simul).
% - solve_DM            [double]        indicator on whether to recompute decision rules
%
% OUTPUT:
% - data                [structure]     simulation result containing fields:
%                                           - linear: paths for endogenous variables ignoring OBC (linear solution)
%                                           - piecewise: paths for endogenous variables satisfying the OBC (occbin/piecewise solution)
%                                           - ys: vector of steady state values
%                                           - regime_history: information on number and time of regime transitions
% - SS_out              [structure]     State space solution
%                                           - T: [n_vars by n_vars by n_shock_period] array of transition matrices
%                                           - R: [n_vars by n_exo by n_shock_period] array of shock response matrices
%                                           - C: [n_vars by n_shock_period] array of constants
% - error_flag          [integer]       1 if a problem was encoutered, 0 otherwise

% Original authors: Luca Guerrieri and Matteo Iacoviello 
% Original file downloaded from:
% https://www.matteoiacoviello.com/research_files/occbin_20140630.zip
% Adapted for Dynare by Dynare Team.
%
% This code is in the public domain and may be used freely.
% However the authors would appreciate acknowledgement of the source by
% citation of any of the following papers:
%
% Luca Guerrieri and Matteo Iacoviello (2015): "OccBin: A toolkit for solving
% dynamic models with occasionally binding constraints easily"
% Journal of Monetary Economics 70, 22-38

persistent DM

if isempty(DM)
    solve_DM=true;
end

data.shocks_sequence = opts_simul_.SHOCKS;                   % sequence of unforeseen shocks under which one wants to solve the model
n_periods = opts_simul_.periods;                        % simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
curb_retrench = opts_simul_.curb_retrench;              % 0: updates guess based on previous iteration; 1: updates similar to Gauss-Jacobi scheme, slowing iterations down by updating guess only one period at a time
max_iter = opts_simul_.maxit;                           % maximum number of iterations allowed for the solution algorithm
endo_init = opts_simul_.endo_init;                      % initial condition for state variables, in deviation from steady state in declaration order
binding_indicator = opts_simul_.init_binding_indicator;    % initial guess for constraint violations
regime_history_guess = opts_simul_.init_regime;              % initial guess for constraint violations
periodic_solution = opts_simul_.periodic_solution;

data.exo_pos = opts_simul_.exo_pos;

n_shocks_periods = size(data.shocks_sequence,1);

if n_periods < n_shocks_periods
    n_periods = n_shocks_periods;
end
nperiods_0 = max(opts_simul_.check_ahead_periods,n_periods-n_shocks_periods);

error_flag=0;

M_base = M_;
dr_base = dr;

% ensure that the two models have the same parameters
% use the parameters for the base model.
%Mstar_.params = Mbase_.params;

data.ys = dr_base.ys;

if solve_DM
    DM.n_vars = M_base.endo_nbr;
    DM.n_exo = M_base.exo_nbr;

    % get the matrices holding the first derivatives for the model
    % each regime is treated separately
    [DM.Cbarmat, DM.Bbarmat, DM.Abarmat, DM.Jbarmat] = occbin.get_deriv(M_base,data.ys);
    
    Mstar_= M_base;
    Mstar_.params(M_.occbin.pswitch(1))= 1;
    [DM.Cstarbarmat, DM.Bstarbarmat, DM.Astarbarmat, DM.Jstarbarmat, DM.Dstarbarmat] = occbin.get_deriv(Mstar_,data.ys);
    
    [DM.decrulea,DM.decruleb]=occbin.get_pq(dr_base);
    
    update_flag=true;    
else
    update_flag=false;
end

endo_names = M_base.endo_names;
exo_names =  M_base.exo_names;


% set some initial conditions and loop through the shocks
% period by period
init_orig_ = endo_init;
zdatapiecewise_ = zeros(n_periods,DM.n_vars);
% nwishes_ = size(wishlist_,1);
if ~exist('binding_indicator','var')
    binding_indicator = false(nperiods_0+1,1);
else
    if length(binding_indicator)<(nperiods_0+1)
        binding_indicator = [binding_indicator; false(nperiods_0+1-length(binding_indicator),1)];
    end
end
SS_out.T = NaN(DM.n_vars,DM.n_vars,n_shocks_periods);
SS_out.R = NaN(DM.n_vars,DM.n_exo,n_shocks_periods);
SS_out.C = nan(DM.n_vars,n_shocks_periods);
if ~exist('regime_history_','var') || isempty(regime_history_guess)
    regime_history = struct();
    guess_history = false;
else
    guess_history = true;
    regime_history = regime_history_guess;
end

if opts_simul_.waitbar
    hh = dyn_waitbar(0,'Occbin: Solving the model');
    set(hh,'Name','Occbin: Solving the model.');
end

for shock_period = 1:n_shocks_periods
    if opts_simul_.waitbar
        dyn_waitbar(shock_period/n_shocks_periods, hh, sprintf('Period %u of %u', shock_period,n_shocks_periods));
    end
    
    regime_change_this_iteration=true;
    iter = 0;
    guess_history_it = false;
    if guess_history && (shock_period<=length(regime_history_guess)) %beyond guess regime history
        guess_history_it = true;
    end
    
    is_periodic=false;
    binding_indicator_history={};
    max_err = NaN(max_iter,1);
    
    while (regime_change_this_iteration && iter<max_iter && ~is_periodic)
        iter = iter +1;
        if binding_indicator(end) && nperiods_0<opts_simul_.max_periods
            binding_indicator = [binding_indicator; false ];
            nperiods_0 = nperiods_0 + 1;
            disp_verbose(['nperiods has been endogenously increased up to ' int2str(nperiods_0) '.'],opts_simul_.debug)
        end
        if length(binding_indicator)<(nperiods_0 + 1)
            binding_indicator=[binding_indicator; false(nperiods_0 + 1-length(binding_indicator),1)];
        end
        
        binding_indicator_history{iter}=binding_indicator;
        
        if iter==1 && guess_history_it
            regime = regime_history_guess(shock_period).regime;
            regime_start = regime_history_guess(shock_period).regimestart;
            binding_indicator(:,1) = regime(end);
            for ir=1:length(regime)-1
                binding_indicator(regime_start(ir):regime_start(ir+1)-1,1) = regime(ir);
            end
            nperiods_0 = size(binding_indicator,1)-1; %if history is present, update may be required
        end
        % analyze when each regime starts based on current guess
        [regime, regime_start, error_code_period]=occbin.map_regime(binding_indicator,opts_simul_.debug);
        regime_history(shock_period).regime = regime;
        regime_history(shock_period).regimestart = regime_start;
        
        % get the hypothesized piece wise linear solution
        if shock_period==1 || shock_period>1 && any(data.shocks_sequence(shock_period,:))
            [zdatalinear_, SS_out.T(:,:,shock_period), SS_out.R(:,:,shock_period), SS_out.C(:,shock_period), SS, update_flag]=occbin.mkdatap_anticipated_dyn(nperiods_0,DM,...
                regime_start(end)-1,binding_indicator,...
                data.exo_pos,data.shocks_sequence(shock_period,:),endo_init,update_flag);
            
            [binding, relax, err]=feval([M_.fname,'.occbin_difference'],zdatalinear_,M_.params,dr_base.ys);

            % check if changes to the hypothesis of the duration for each
            % regime
            if any(binding.constraint_1 & ~binding_indicator) || any(relax.constraint_1 & binding_indicator)
                err_viol = err.binding_constraint_1(binding.constraint_1 & ~binding_indicator);
                err_relax = err.relax_constraint_1(relax.constraint_1 & binding_indicator);
                max_err(iter) = max(abs([err_viol;err_relax]));
                regime_change_this_iteration = true;
            else
                regime_change_this_iteration = false;
                max_err(iter) = 0;
            end

            if curb_retrench   % apply Gauss-Seidel idea of slowing down the change in the guess
                % for the constraint -- only relax one
                % period at a time starting from the last
                % one when each of the constraints is true.
                retrench = false(numel(binding_indicator),1);
                max_relax_constraint_1=find(relax.constraint_1 & binding_indicator,1,'last');
                if ~isempty(max_relax_constraint_1) && find(relax.constraint_1,1,'last')>=find(binding_indicator,1,'last')
                    retrench(max_relax_constraint_1) = true;
                end                
                binding_indicator = (binding_indicator | binding.constraint_1) & ~ retrench;
           else
                binding_indicator= (binding_indicator | binding.constraint_1) & ~(binding_indicator & relax.constraint_1);
            end
            
            if iter>1 && regime_change_this_iteration
                for kiter=1:iter-1
                    vvv = [binding_indicator_history{kiter}; false(size(binding_indicator,1)- size(binding_indicator_history{kiter},1), 1)];
                    is_periodic(kiter) = isequal(vvv, binding_indicator);
                end
                is_periodic_all =is_periodic;
                is_periodic =  any(is_periodic);
                if is_periodic && periodic_solution
                    [merr,imerr]=min(max_err(find(is_periodic_all,1):end));
                    inx = find(is_periodic_all,1):iter;
                    inx = inx(imerr);
                    binding_indicator=binding_indicator_history{inx};
                    if inx<iter
                        [regime, regime_start, error_code_period]=occbin.map_regime(binding_indicator,opts_simul_.debug);
                        regime_history(shock_period).regime = regime;
                        regime_history(shock_period).regimestart = regime_start;
                        
                        % get the hypothesized piece wise linear solution
                        [zdatalinear_, SS_out.T(:,:,shock_period), SS_out.R(:,:,shock_period), SS_out.C(:,shock_period), SS, update_flag]=occbin.mkdatap_anticipated_dyn(nperiods_0,DM,...
                            regime_start(end)-1,binding_indicator,...
                            data.exo_pos,data.shocks_sequence(shock_period,:),endo_init,update_flag);                        
                    end                    
                end
            end
            
        else
            regime_change_this_iteration=false;
            zdatalinear_(1:end-1,:)=zdatalinear_(2:end,:);
            zdatalinear_(end,:) = DM.decrulea*zdatalinear_(end-1,:)';
            if length(SS)>1
                SS=SS(2:end);
            else
                SS=[];
            end
            if isempty(SS)
                SS_out.T(:,:,shock_period)= DM.decrulea;
                SS_out.R(:,:,shock_period)= DM.decruleb;
                SS_out.C(:,shock_period)= 0;
            else
                SS_out.T(:,:,shock_period)= SS(1).T;
                SS_out.R(:,:,shock_period)= SS(1).R;
                SS_out.C(:,shock_period)= SS(1).C;
            end
            binding_indicator_history{iter}=binding_indicator;
        end
        
    end
    
    if regime_change_this_iteration ==1 && max_iter>1
        disp_verbose(['occbin solver:: period ' int2str(shock_period) '::'],opts_simul_.debug)
        if is_periodic
            disp_verbose('Occbin solver loops between two regimes.',opts_simul_.debug)
            if periodic_solution
                disp_verbose(['Max error:' num2str(merr) '.'],opts_simul_.debug)
            else
                if opts_simul_.waitbar; dyn_waitbar_close(hh); end
                error_flag = 310;
                return
            end
        else
            disp_verbose('Did not converge -- increase maxit.',opts_simul_.debug)
            if opts_simul_.waitbar; dyn_waitbar_close(hh); end
            error_flag = 311;
            return
        end
    end
    if any(error_code_period)
        disp_verbose('Increase nperiods.',opts_simul_.debug)
        if opts_simul_.waitbar; dyn_waitbar_close(hh); end
        error_flag = 312;
        return
    end
    
    endo_init = zdatalinear_(1,:);
    zdatapiecewise_(shock_period,:)=endo_init;
    endo_init= endo_init';
    
    % reset binding_indicator for next period's shock -- this resetting is
    % consistent with expecting no additional shocks
    binding_indicator=[binding_indicator(2:end); false];
    
end

% if necessary, fill in the rest of the path with the remainder of the
% last IRF computed.
zdatapiecewise_(n_shocks_periods+1:end,:)=zdatalinear_(2:n_periods-n_shocks_periods+1,:);

data.piecewise=zdatapiecewise_;
data.regime_history=regime_history;

if ~opts_simul_.piecewise_only
    % get the linear responses
    data.linear = occbin.mkdata(max(n_periods,size(data.shocks_sequence,1)),...
        DM.decrulea,DM.decruleb,endo_names,exo_names,...
        [],data.exo_pos,data.shocks_sequence,init_orig_);
end

if opts_simul_.waitbar
    dyn_waitbar_close(hh); 
end