68 lines
3.5 KiB
Matlab
68 lines
3.5 KiB
Matlab
function [forcs, e]= 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
|
|
%
|
|
% INPUTS
|
|
% 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 [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:
|
|
% y_t=T*y_{t-1}+R*shocks(:,t)
|
|
% Shocks are split up into shocks_uncontrolled and shockscontrolled while
|
|
% the endogenous variables are also split up into controlled and
|
|
% uncontrolled ones to get:
|
|
% y_t(controlled_vars_index)=T*y_{t-1}(controlled_vars_index)+R(controlled_vars_index,uncontrolled_shocks_index)*shocks_uncontrolled_t
|
|
% + R(controlled_vars_index,controlled_shocks_index)*shocks_controlled_t
|
|
%
|
|
% This is then solved to get:
|
|
% shocks_controlled_t=(y_t(controlled_vars_index)-(T*y_{t-1}(controlled_vars_index)+R(controlled_vars_index,uncontrolled_shocks_index)*shocks_uncontrolled_t)/R(controlled_vars_index,controlled_shocks_index)
|
|
%
|
|
% Variable number of controlled vars are allowed in different
|
|
% periods. Missing control information are indicated by NaN in
|
|
% y_t(controlled_vars_index).
|
|
%
|
|
% After obtaining the shocks, and for uncontrolled periods, the state-space representation
|
|
% y_t=T*y_{t-1}+R*shocks(:,t)
|
|
% is used for forecasting
|
|
%
|
|
% Copyright (C) 2006-2017 Dynare Team
|
|
%
|
|
% This file is part of Dynare.
|
|
%
|
|
% Dynare is free software: you can redistribute it and/or modify
|
|
% it under the terms of the GNU General Public License as published by
|
|
% the Free Software Foundation, either version 3 of the License, or
|
|
% (at your option) any later version.
|
|
%
|
|
% Dynare is distributed in the hope that it will be useful,
|
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
% GNU General Public License for more details.
|
|
%
|
|
% You should have received a copy of the GNU General Public License
|
|
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
if cL
|
|
e = zeros(size(mcValue,1),cL);
|
|
for t = 1:cL
|
|
% missing conditional values are indicated by NaN
|
|
k = find(isfinite(mcValue(:,t)));
|
|
e(k,t) = inv(mv(k,:)*R*mu(:,k))*(mcValue(k,t)-mv(k,:)*T*forcs(:,t)-mv(k,:)*R*shocks(:,t));
|
|
forcs(:,t+1) = T*forcs(:,t)+R*(mu(:,k)*e(k,t)+shocks(:,t));
|
|
end
|
|
end
|
|
for t = cL+1:H
|
|
forcs(:,t+1) = T*forcs(:,t)+R*shocks(:,t);
|
|
end |