85 lines
3.6 KiB
Matlab
85 lines
3.6 KiB
Matlab
function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,DynareOptions,DynareOutput,IntegrationMethod)
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pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
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pfm.ny = DynareModel.endo_nbr;
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pfm.Sigma = DynareModel.Sigma_e;
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pfm.Omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
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pfm.number_of_shocks = length(pfm.Sigma);
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pfm.stochastic_order = DynareOptions.ep.stochastic.order;
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pfm.max_lag = DynareModel.maximum_endo_lag;
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if pfm.max_lag > 0
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pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
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pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
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else
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pfm.nyp = 0;
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pfm.iyp = [];
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end
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pfm.ny0 = nnz(pfm.lead_lag_incidence(pfm.max_lag+1,:));
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pfm.iy0 = find(pfm.lead_lag_incidence(pfm.max_lag+1,:)>0);
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if DynareModel.maximum_endo_lead
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pfm.nyf = nnz(pfm.lead_lag_incidence(pfm.max_lag+2,:));
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pfm.iyf = find(pfm.lead_lag_incidence(pfm.max_lag+2,:)>0);
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else
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pfm.nyf = 0;
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pfm.iyf = [];
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end
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pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
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pfm.nrc = pfm.nyf+1;
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pfm.isp = [1:pfm.nyp];
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pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
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pfm.isf = pfm.iyf+pfm.nyp;
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pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
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pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
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pfm.periods = DynareOptions.ep.periods;
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pfm.steady_state = DynareOutput.steady_state;
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pfm.params = DynareModel.params;
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if DynareModel.maximum_endo_lead
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pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
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pfm.i_cols_A1 = find(pfm.lead_lag_incidence(pfm.max_lag+(1:2),:)');
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else
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pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(pfm.max_lag+1,:)');
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pfm.i_cols_A1 = find(pfm.lead_lag_incidence(pfm.max_lag+1,:)');
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end
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if pfm.max_lag > 0
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pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
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else
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pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1,:)');
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end
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pfm.i_cols_j = 1:pfm.nd;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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pfm.dynamic_model = str2func([DynareModel.fname,'_dynamic']);
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pfm.verbose = DynareOptions.ep.verbosity;
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pfm.maxit_ = DynareOptions.maxit_;
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pfm.tolerance = DynareOptions.dynatol.f;
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if nargin>3 && DynareOptions.ep.stochastic.order
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% Compute weights and nodes for the stochastic version of the extended path.
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switch IntegrationMethod
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case 'Tensor-Gaussian-Quadrature'
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% Get the nodes and weights from a univariate Gauss-Hermite quadrature.
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[nodes,weights] = gauss_hermite_weights_and_nodes(DynareOptions.ep.stochastic.quadrature.nodes);
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% Replicate the univariate nodes for each innovation and dates, and, if needed, correlate them.
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nodes = repmat(nodes,1,pfm.number_of_shocks*pfm.stochastic_order)*kron(eye(pfm.stochastic_order),pfm.Omega);
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% Put the nodes and weights in cells
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for i=1:pfm.number_of_shocks
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rr(i) = {nodes(:,i)};
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ww(i) = {weights};
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end
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% Build the tensorial grid
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pfm.nodes = cartesian_product_of_sets(rr{:});
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pfm.weights = prod(cartesian_product_of_sets(ww{:}),2);
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pfm.nnodes = length(pfm.weights);
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case 'Stroud-Cubature-3'
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[nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,3,'Stroud')
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pfm.nodes = kron(eye(pfm.stochastic_order),transpose(Omega))*nodes;
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pfm.weights = weights;
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pfm.nnodes = length(pfm.weights);
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case 'Stroud-Cubature-5'
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[nodes,weights] = cubature_with_gaussian_weight(pfm.number_of_shocks*pfm.stochastic_order,5,'Stroud')
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pfm.nodes = kron(eye(pfm.stochastic_order),transpose(Omega))*nodes;
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pfm.weights = weights;
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pfm.nnodes = length(weights);
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otherwise
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error('setup_stochastic_perfect_foresight_model_solver:: Unknown integration algorithm!')
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end
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end |