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conditional forecasts: add check whether simulation was successful

time-shift
Johannes Pfeifer 2020-02-12 11:04:46 +01:00
parent 181725c778
commit 228b2a532f
2 changed files with 15 additions and 12 deletions
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9
matlab/imcforecast.m
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@ -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);

18
matlab/mcforecast3.m
View File

@ -1,5 +1,5 @@
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: