conditional forecasts: add check whether simulation was successful

matlab/imcforecast.m

@@ -204,9 +204,6 @@ if ~estimated_model
     trend = repmat(ys(oo_.dr.order_var,:),1,options_cond_fcst.periods+1); %trend needs to contain correct steady state
 end
 
-
-
-
 NumberOfStates = length(InitState);
 FORCS1 = zeros(NumberOfStates,options_cond_fcst.periods+1,options_cond_fcst.replic);
 
@@ -252,7 +249,11 @@ for b=1:options_cond_fcst.replic %conditional forecast using cL set to constrain
     [FORCS1(:,:,b), FORCS1_shocks(:,:,b)] = mcforecast3(cL,options_cond_fcst.periods,constrained_paths,shocks,FORCS1(:,:,b),T,R,mv, mu);
     FORCS1(:,:,b)=FORCS1(:,:,b)+trend; %add trend
 end
-
+if max(max(max(abs(FORCS1(constrained_vars,1:cL,:)-constrained_paths))))>1e-4
+    fprintf('\nconditional_forecasts: controlling of variables was not successful.\n')
+    fprintf('This can be due to numerical imprecision (e.g. explosive simulations)\n')
+    fprintf('or because the instrument(s) do not allow controlling the variable(s).\n')
+end
 mFORCS1 = mean(FORCS1,3);
 mFORCS1_shocks = mean(FORCS1_shocks,3);
 

matlab/mcforecast3.m

 function [forcs, e]= mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
-% forcs = mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
+% [forcs, e] = mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
 % Computes the shock values for constrained forecasts necessary to keep
 % endogenous variables at their constrained paths
 %
@@ -7,13 +7,15 @@ function [forcs, e]= mcforecast3(cL,H,mcValue,shocks,forcs,T,R,mv,mu)
 %  o cL             [scalar]                            number of controlled periods
 %  o H              [scalar]                            number of forecast periods
 %  o mcValue        [n_controlled_vars by cL double]    paths for constrained variables
-%  o shocks         [nexo by H double]      shock values draws (with zeros for controlled_varexo)
-%  o forcs
-%  o T              [n_endovars by n_endovars double]       transition matrix of the state equation.
-%  o R              [n_endovars by n_exo double]           matrix relating the endogenous variables to the innovations in the state equation.
-%  o mv             [n_controlled_exo by n_endovars boolean]        indicator vector  selecting constrained endogenous variables
-%  o mu             [n_controlled_vars by nexo boolean]             indicator vector
-%                                                   selecting controlled exogenous variables
+%  o shocks         [nexo by H double]                  shock values draws (with zeros for controlled_varexo)
+%  o forcs          [n_endovars by H+1 double]          matrix of endogenous variables storing the inital condition
+%  o T              [n_endovars by n_endovars double]   transition matrix of the state equation.
+%  o R              [n_endovars by n_exo double]        matrix relating the endogenous variables to the innovations in the state equation.
+%  o mv             [n_controlled_exo by n_endovars boolean]   indicator vector  selecting constrained endogenous variables
+%  o mu             [n_controlled_vars by nexo boolean]        indicator vector selecting controlled exogenous variables
+% OUTPUTS
+%  o forcs          [n_endovars by H+1 double]          matrix of forecasted endogenous variables
+%  o e              [nexo by H double]                  matrix of exogenous variables
 %
 % Algorithm:
 %   Relies on state-space form: