diff --git a/matlab/backward/backward_model_irf.m b/matlab/backward/backward_model_irf.m
index 07f01e14bfbc8288a55a47fb8e6c19e4487fb56c..593311a1d4c2e33ef64163e6827a521752eed709 100644
--- a/matlab/backward/backward_model_irf.m
+++ b/matlab/backward/backward_model_irf.m
@@ -1,22 +1,23 @@
-function irfs = backward_model_irf(initialcondition, listofshocks, listofvariables, varargin)
+function [deviations, baseline, irfs] = backward_model_irf(initialcondition, innovationbaseline, listofshocks, listofvariables, varargin)
% Returns impulse response functions.
%
-% INPUTS
-% - initialcondition [dseries,dates] Initial conditions for the endogenous variables, or period 0.
-% - listofshocks [cell of strings] The innovations for which the IRFs need to be computed.
-% - listofvariables [cell of strings] The endogenous variables which will be returned.
+% INPUTS
+% - initialcondition [dseries] Initial conditions for the endogenous variables, or period 0.
+% - innovationbaseline [dseries] Baseline for the future innovations. If empty the baseline scenario is zero for future shocks.
+% - listofshocks [cell of strings] The innovations for which the IRFs need to be computed.
+% - listofvariables [cell of strings] The endogenous variables which will be returned.
% - periods [integer] scalar, the number of periods.
%
-% OUTPUTS
-% - irfs [struct of dseries]
+% OUTPUTS
+% - irfs [struct of dseries]
%
-% REMARKS
-% The names of the fields in the returned structure are given by the name
-% of the innovations listed in the second input argument. Each field gather
-% the associated paths for endogenous variables listed in the third input
-% argument.
-
+% REMARKS
+% - The names of the fields in the returned structure are given by the name
+% of the innovations listed in the second input argument. Each field gather
+% the associated paths for endogenous variables listed in the third input
+% argument.
+% - If second argument is not empty, periods must not be greater than innovationbaseline.nobs.
% Copyright (C) 2017 Dynare Team
%
@@ -43,7 +44,7 @@ if M_.maximum_lead
end
% Set default value for the fourth input argument.
-if nargin<4
+if nargin<5
periods = 40;
notransform = true;
else
@@ -51,39 +52,78 @@ else
end
% Set default value for the last input argument (no transformation).
-if nargin<5
+if nargin<6
notransform = true;
else
notransform = false;
transform = varargin{2};
end
+baselineflag = false;
+% Set default values for the baseline paths.
+%
+% TODO zero for all variables is probably a poor choice. It should be
+% zero for additive exogenous variables and 1 for multiplicative
+% exogenous variables.
+Innovations = zeros(periods, M_.exo_nbr);
+
+if ~isempty(innovationbaseline)
+ if ~isdseries(innovationbaseline)
+ error('If not empty, the second argument has to be a dseries object!')
+ end
+ if ~isequal(innovationbaseline.dates(1)-initialcondition.dates(end), 1)
+ error('The first date of the second input argument must follow the last date of the first input argument!')
+ end
+ if innovationbaseline.nobs<periods
+ error('The second input argument must at least have %s observations or lower the number of periods.', periods)
+ end
+ % Fill innovations with provided paths for the innovations.
+ exonames = cellstr(M_.exo_names);
+ for i = 1:length(exonames)
+ if ~isempty(strmatch(exonames{i}, innovationbaseline.name))
+ Innovations(:,i) = innovationbaseline{exonames{i}}.data(1:periods);
+ end
+ end
+ baselineflag = true;
+end
+
% Set up initial conditions
-[initialcondition, periods, innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, nx, ny1, iy1, jdx, model_dynamic, y] = ...
- simul_backward_model_init(initialcondition, periods, options_, M_, oo_, zeros(periods, M_.exo_nbr));
+[initialcondition, periods, Innovations, DynareOptions, DynareModel, DynareOutput, endonames, exonames, nx, ny1, iy1, jdx, model_dynamic, y] = ...
+ simul_backward_model_init(initialcondition, periods, options_, M_, oo_, Innovations);
% Get the covariance matrix of the shocks.
Sigma = M_.Sigma_e + 1e-14*eye(M_.exo_nbr);
sigma = transpose(chol(Sigma));
% Initialization of the returned argument. Each will be a dseries object containing the IRFS for the endogenous variables listed in the third input argument.
+deviations = struct();
+baseline = dseries();
irfs = struct();
+% Baseline paths (get transition paths induced by the initial condition and
+% baseline innovations).
+if options_.linear
+ ysim__0 = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, Innovations, nx, ny1, iy1, jdx, model_dynamic);
+else
+ ysim__0 = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, Innovations, iy1, model_dynamic);
+end
+
+% Transform the endogenous variables.
+if notransform
+ endo_simul__0 = ysim__0;
+else
+ endo_simul__0 = feval(transform, ysim__0);
+end
+
% Compute the IRFs (loop over innovations).
for i=1:length(listofshocks)
- innovations = zeros(periods, DynareModel.exo_nbr);
- % Get transition paths induced by the initial condition.
- if options_.linear
- ysim__0 = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
- else
- ysim__0 = simul_backward_nonlinear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, iy1, model_dynamic);
- end
+ innovations = Innovations;
% Add the shock.
j = find(strcmp(listofshocks{i}, exonames));
if isempty(j)
error('backward_model_irf: Exogenous variable %s is unknown!', listofshocks{i})
end
- innovations(1,:) = transpose(sigma(:,j));
+ innovations(1,:) = innovations(1,:) + transpose(sigma(:,j));
if options_.linear
ysim__1 = simul_backward_linear_model_(initialcondition, periods, DynareOptions, DynareModel, DynareOutput, innovations, nx, ny1, iy1, jdx, model_dynamic);
else
@@ -91,14 +131,22 @@ for i=1:length(listofshocks)
end
% Transform the endogenous variables
if notransform
- endo_simul__0 = ysim__0;
endo_simul__1 = ysim__1;
else
- endo_simul__0 = feval(transform, ysim__0);
endo_simul__1 = feval(transform, ysim__1);
end
% Instantiate a dseries object (with all the endogenous variables)
- allirfs = dseries(transpose(endo_simul__1-endo_simul__0), initialcondition.init, endonames, cellstr(DynareModel.endo_names_tex));
+ alldeviations = dseries(transpose(endo_simul__1-endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), cellstr(DynareModel.endo_names_tex(1:M_.orig_endo_nbr,:)));
+ if nargout>2
+ allirfs = dseries(transpose(endo_simul__1), initialcondition.init, endonames(1:M_.orig_endo_nbr), cellstr(DynareModel.endo_names_tex(1:M_.orig_endo_nbr,:)));
+ end
% Extract a sub-dseries object
- irfs.(listofshocks{i}) = allirfs{listofvariables{:}};
+ deviations.(listofshocks{i}) = alldeviations{listofvariables{:}};
+ if nargout>2
+ irfs.(listofshocks{i}) = allirfs{listofvariables{:}};
+ end
+end
+
+if nargout>1
+ baseline = dseries(transpose(endo_simul__0), initialcondition.init, endonames(1:M_.orig_endo_nbr), cellstr(DynareModel.endo_names_tex(1:M_.orig_endo_nbr,:)));
end
\ No newline at end of file