diff --git a/matlab/backward/backward_model_forecast.m b/matlab/backward/backward_model_forecast.m
index 294549d6653b74a787f4f774821c39fff68c46da..0c23abaefc3f6b441235649f8605af4fa98f62eb 100644
--- a/matlab/backward/backward_model_forecast.m
+++ b/matlab/backward/backward_model_forecast.m
@@ -79,11 +79,27 @@ if withuncertainty
sigma = transpose(chol(Sigma));
end
+% Set initial condition.
+if isdates(initialcondition)
+ if isempty(M_.endo_histval)
+ error('backward_model_irf: histval block for setting initial condition is missing!')
+ end
+ initialcondition = dseries(transpose(M_.endo_histval), initialcondition, endo_names, cellstr(M_.endo_names_tex));
+end
+
% Put initial conditions in a vector of doubles
initialconditions = transpose(initialcondition{endo_names{:}}.data);
% Compute forecast without shock
-innovations = zeros(periods+1, M_.exo_nbr);
+innovations = zeros(periods+max(M_.maximum_exo_lag, 1), M_.exo_nbr);
+if M_.maximum_exo_lag
+ if isempty(M_.exo_histval)
+ error('You need to set the past values for the exogenous variables!')
+ else
+ innovations(1:M_.maximum_exo_lag, :) = M_.exo_histval;
+ end
+end
+
oo__0 = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);
forecasts.pointforecast = dseries(transpose(oo__0.endo_simul(idy,:)), initialcondition.init, listofvariables);
@@ -91,7 +107,7 @@ if withuncertainty
% Preallocate an array gathering the simulations.
ArrayOfForectasts = zeros(n, periods+1, B);
for i=1:B
- innovations(2:end,:) = transpose(sigma*randn(M_.exo_nbr, periods));
+ innovations(max(M_.maximum_exo_lag, 1)+1:end,:) = transpose(sigma*randn(M_.exo_nbr, periods));
oo__ = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);
ArrayOfForecasts(:,:,i) = oo__.endo_simul(idy,:);
end
diff --git a/matlab/backward/backward_model_irf.m b/matlab/backward/backward_model_irf.m
index 3ee05024b91e77e6c0615e39fef4d97eb1349290..043568351aec00a28c5c7227ba0aa2c57189c037 100644
--- a/matlab/backward/backward_model_irf.m
+++ b/matlab/backward/backward_model_irf.m
@@ -1,9 +1,9 @@
-function irfs = backward_model_irf(initialcondition, listofshocks, listofvariables, periods, transform)
+function irfs = backward_model_irf(initialcondition, listofshocks, listofvariables, varargin)
% Returns impulse response functions.
%
% INPUTS
-% - initialcondition [dseries] Initial conditions for the endogenous variables.
+% - 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.
% - periods [integer] scalar, the number of periods.
@@ -42,19 +42,12 @@ if M_.maximum_lead
error(['simul_model_irf:: The specified model is not backward looking!'])
end
-% Set the number of innovations and variables for which we want to compute the IRFs.
-nx = length(listofshocks);
-ny = length(listofvariables);
-
-% Initialization of the returned argument. Each will be a dseries
-% object containing the IRFS for the endogenous variables listed in the
-% third input argument.
-irfs = struct();
-
% Set default value for the fourth input argument.
if nargin<4
periods = 40;
notransform = true;
+else
+ periods = varargin{1};
end
% Set default value for the last input argument (no transformation).
@@ -62,6 +55,7 @@ if nargin<5
notransform = true;
else
notransform = false;
+ transform = varargin{2};
end
% Get list of all exogenous variables in a cell of strings
@@ -85,20 +79,33 @@ sigma = transpose(chol(Sigma));
% Put initial conditions in a vector of doubles
initialconditions = transpose(initialcondition{endo_names{:}}.data);
+% Initialization of the returned argument. Each will be a dseries object containing the IRFS for the endogenous variables listed in the third input argument.
+irfs = struct();
+
+% Get the covariance matrix of the shocks.
+Sigma = M_.Sigma_e + 1e-14*eye(M_.exo_nbr);
+sigma = transpose(chol(Sigma));
+
+% Put initial conditions in a vector of doubles
+initialconditions = transpose(initialcondition{endo_names{:}}.data);
+
% Compute the IRFs (loop over innovations).
for i=1:length(listofshocks)
% Get transition paths induced by the initial condition.
- innovations = zeros(periods+1, M_.exo_nbr);
+ innovations = zeros(periods+M_.maximum_exo_lag, M_.exo_nbr);
+ if ~isempty(M_.exo_histval)
+ innovations(1:M_.maximum_exo_lag,:) = M_.exo_histval;
+ end
oo__0 = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);
% Add the shock.
j = strmatch(listofshocks{i}, exo_names);
if isempty(j)
error('backward_model_irf: Exogenous variable %s is unknown!', listofshocks{i})
end
- innovations(2,:) = transpose(sigma(:,j));
+ innovations(1+M_.maximum_exo_lag,:) = transpose(sigma(:,j));
oo__1 = simul_backward_model(initialconditions, periods, options_, M_, oo_, innovations);
% Transform the endogenous variables
- if notransform
+ if notransform
endo_simul__0 = oo__0.endo_simul;
endo_simul__1 = oo__1.endo_simul;
else
diff --git a/matlab/backward/simul_backward_model.m b/matlab/backward/simul_backward_model.m
index 59fc0e5aed4dc5d29be4dab2041d163e5fec36ba..4cf801fbdb6532dd9a7e82ea76f2d2e9efe866b3 100644
--- a/matlab/backward/simul_backward_model.m
+++ b/matlab/backward/simul_backward_model.m
@@ -76,10 +76,14 @@ if nargin<6
otherwise
error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
end
-
% Put the simulated innovations in DynareOutput.exo_simul.
- DynareOutput.exo_simul = zeros(sample_size+1,number_of_shocks);
+ DynareOutput.exo_simul = zeros(sample_size, number_of_shocks);
DynareOutput.exo_simul(2:end,positive_var_indx) = DynareOutput.bnlms.shocks;
+ if isfield(DynareModel,'exo_histval') && ~isempty(DynareModel.exo_histval)
+ DynareOutput.exo_simul = [M_.exo_histval; DynareOutput.exo_simul];
+ else
+ DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
+ end
else
number_of_shocks = size(innovations,2);
DynareOutput.exo_simul = innovations;
@@ -91,5 +95,4 @@ if DynareOptions.linear
else
DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, ...
DynareModel, DynareOutput, innovations);
-end
-
\ No newline at end of file
+end
\ No newline at end of file
diff --git a/matlab/backward/simul_backward_nonlinear_model.m b/matlab/backward/simul_backward_nonlinear_model.m
index fbd6241d1157a16d89b0652a5fed98ad4a2c3515..6ac2cfbbd36ddb0ab2c3ec4e1d1b9a9e063fceb4 100644
--- a/matlab/backward/simul_backward_nonlinear_model.m
+++ b/matlab/backward/simul_backward_nonlinear_model.m
@@ -64,7 +64,11 @@ if nargin<6
% Put the simulated innovations in DynareOutput.exo_simul.
DynareOutput.exo_simul = zeros(sample_size,number_of_shocks);
DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
- DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
+ if isfield(DynareModel,'exo_histval') && ~ isempty(DynareModel.exo_histval)
+ DynareOutput.exo_simul = [M_.exo_histval; DynareOutput.exo_simul];
+ else
+ DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
+ end
else
DynareOutput.exo_simul = innovations;
end
@@ -86,7 +90,7 @@ y = NaN(length(idx)+ny1,1);
% initialization of the returned simulations.
DynareOutput.endo_simul = NaN(DynareModel.endo_nbr,sample_size+1);
if isempty(initial_conditions)
- if isfield(DynareModel,'endo_histval')
+ if isfield(DynareModel,'endo_histval') && ~isempty(DynareModel.endo_histval)
DynareOutput.endo_simul(:,1:DynareModel.maximum_lag) = DynareModel.endo_histval;
else
warning('simul_backward_nonlinear_model:: Initial condition is zero for all variables! If the model is nonlinear, the model simulation may fail with the default initialization')
@@ -97,11 +101,12 @@ else
end
Y = DynareOutput.endo_simul;
+
% Simulations (call a Newton-like algorithm for each period).
for it = 2:sample_size+1
ylag = Y(iy1,it-1); % Set lagged variables.
y = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
- Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
+ Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it+(DynareModel.maximum_exo_lag-1));
end
DynareOutput.endo_simul = Y;
\ No newline at end of file