Commit c9469a36 authored by Stéphane Adjemian's avatar Stéphane Adjemian
Browse files

Buikd the tree of future shocks in setup_stochastic_perfect_foresight_model_solver routine.

parent 74c856b7
function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,DynareOptions,DynareOutput,Algorithm,IntegrationMethod)
function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,DynareOptions,DynareOutput,IntegrationMethod)
pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
pfm.ny = DynareModel.endo_nbr;
pfm.Sigma_e = DynareModel.Sigma_e;
pfm.Sigma = DynareModel.Sigma_e;
pfm.omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
pfm.number_of_shocks = length(pfm.Sigma);
pfm.stochastic_order = DynareOptions.ep.stochastic.order;
pfm.max_lag = DynareModel.maximum_endo_lag;
if pfm.max_lag > 0
pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
......@@ -48,3 +51,35 @@ pfm.dynamic_model = str2func([DynareModel.fname,'_dynamic']);
pfm.verbose = DynareOptions.ep.verbosity;
pfm.maxit_ = DynareOptions.maxit_;
pfm.tolerance = DynareOptions.dynatol.f;
if nargin>3
% Compute weights and nodes for the stochastic version of the extended path.
switch IntegrationMethod
case 'Tensor-Gaussian-Quadrature'
% Get the nodes and weights from a univariate Gauss-Hermite quadrature.
[nodes,weights] = gauss_hermite_weights_and_nodes(DynareOptions.ep.stochastic.quadrature.nodes);
% Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
nodes = repmat(nodes,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
% Put the nodes and weights in cells
for i=1:number_of_shocks
rr(i) = {nodes(:,i)};
ww(i) = {weights};
end
% Build the tensorial grid
pfm.nodes = cartesian_product_of_sets(rr{:});
pfm.weights = prod(cartesian_product_of_sets(ww{:}),2);
pfm.nnodes = length(pfm.weights);
case 'Stroud-Cubature-3'
[nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
pfm.nodes = kron(eye(pfm.stochastic_order),transpose(Omega))*nodes;
pfm.weights = weights;
pfm.nnodes = length(pfm.weights);
case 'Stroud-Cubature-5'
[nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
pfm.nodes = kron(eye(pfm.stochastic_order),transpose(Omega))*nodes;
pfm.weights = weights;
pfm.nnodes = length(weights);
otherwise
error('setup_stochastic_perfect_foresight_model_solver:: Unknown integration algorithm!')
end
end
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