make extended path algorithm 1 as a self contained problem usable by dynare_solve
Michel Juillard
parent
c3efb214ef
commit
26f2b301b0
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@ -0,0 +1,194 @@
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function [res,A,info] = ep_problem_2(y,x,pm)
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info = 0;
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res = [];
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A = [];
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dynamic_model = pm.dynamic_model;
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ny = pm.ny;
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params = pm.params;
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steady_state = pm.steady_state;
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order = pm.order;
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nodes = pm.nodes;
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nnodes = pm.nnodes;
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weights = pm.weights;
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h_correction = pm.h_correction;
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dimension = pm.dimension;
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world_nbr = pm.world_nbr;
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nnzA = pm.nnzA;
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periods = pm.periods;
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i_rows = pm.i_rows';
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i_cols = pm.i_cols;
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nyp = pm.nyp;
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nyf = pm.nyf;
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hybrid_order = pm.hybrid_order;
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i_cols_1 = pm.i_cols_1;
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i_cols_j = pm.i_cols_j;
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icA = pm.icA;
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i_cols_T = pm.i_cols_T;
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i_cols_p = i_cols(1:nyp);
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i_cols_s = i_cols(nyp+(1:ny));
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i_cols_f = i_cols(nyp+ny+(1:nyf));
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i_cols_A = i_cols;
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i_cols_Ap0 = i_cols_p;
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i_cols_As = i_cols_s;
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i_cols_Af0 = i_cols_f - ny;
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i_hc = i_cols_f - 2*ny;
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nzA = cell(periods,world_nbr);
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res = zeros(ny,periods,world_nbr);
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Y = zeros(ny*(periods+2),world_nbr);
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Y(1:ny,1) = pm.y0;
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Y(end-ny+1:end,:) = repmat(steady_state,1,world_nbr);
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Y(pm.i_upd_y) = y;
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offset_r0 = 0;
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for i = 1:order+1
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i_w_p = 1;
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for j = 1:(1+(nnodes-1)*(i-1))
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innovation = x;
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if i <= order && j == 1
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% first world, integrating future shocks
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if nargin > 1
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A1 = sparse([],[],[],i*ny,dimension,nnzA*world_nbr);
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end
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for k=1:nnodes
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if nargin > 1
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if i == 2
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i_cols_Ap = i_cols_Ap0;
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elseif i > 2
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i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes- ...
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1)*(i-2)*(i-3)/2);
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end
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if k == 1
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i_cols_Af = i_cols_Af0 + ny*(i-1+(nnodes-1)*i*(i-1)/ ...
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2);
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else
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i_cols_Af = i_cols_Af0 + ny*(i-1+(nnodes-1)*i*(i-1)/ ...
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2+(i-1)*(nnodes-1) ...
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+ k - 1);
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end
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end
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if i > 1
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innovation(i+1,:) = nodes(k,:);
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end
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if k == 1
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k1 = 1;
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else
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k1 = (nnodes-1)*(i-1)+k;
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end
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if hybrid_order == 2 && (k > 1 || i == order)
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z = [Y(i_cols_p,1);
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Y(i_cols_s,1);
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Y(i_cols_f,k1)+h_correction(i_hc)];
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else
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z = [Y(i_cols_p,1);
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Y(i_cols_s,1);
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Y(i_cols_f,k1)];
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end
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if nargin > 1
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[d1,jacobian] = dynamic_model(z,innovation,params,steady_state,i+1);
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if i == 1
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% in first period we don't keep track of
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% predetermined variables
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i_cols_A = [i_cols_As - ny; i_cols_Af];
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A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
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else
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i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
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A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
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end
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else
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d1 = dynamic_model(z,innovation,params,steady_state,i+1);
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end
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res(:,i,1) = res(:,i,1)+weights(k)*d1;
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end
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if nargin > 1
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[ir,ic,v] = find(A1);
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nzA{i,j} = [ir,ic,v]';
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end
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elseif j > 1 + (nnodes-1)*(i-2)
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% new world, using previous state of world 1 and hit
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% by shocks from nodes
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if nargin > 1
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i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes-1)*(i-2)*(i-3)/2);
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i_cols_Af = i_cols_Af0 + ny*(i+(nnodes-1)*i*(i-1)/2+j-2);
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end
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k = j - (nnodes-1)*(i-2);
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innovation(i+1,:) = nodes(k,:);
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z = [Y(i_cols_p,1);
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Y(i_cols_s,j);
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Y(i_cols_f,j)];
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if nargin > 1
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[d1,jacobian] = dynamic_model(z,innovation,params,steady_state,i+1);
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i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
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[ir,ic,v] = find(jacobian(:,i_cols_j));
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nzA{i,j} = [i_rows(ir),i_cols_A(ic), v]';
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else
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d1 = dynamic_model(z,innovation,params,steady_state,i+1);
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end
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res(:,i,j) = d1;
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if nargin > 1
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i_cols_Af = i_cols_Af + ny;
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end
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else
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% existing worlds other than 1
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if nargin > 1
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i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes-1)*(i-2)*(i-3)/2+j-1);
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i_cols_Af = i_cols_Af0 + ny*(i+(nnodes-1)*i*(i-1)/2+j-2);
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end
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z = [Y(i_cols_p,j);
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Y(i_cols_s,j);
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Y(i_cols_f,j)];
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if nargin > 1
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[d1,jacobian] = dynamic_model(z,innovation,params,steady_state,i+1);
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i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
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[ir,ic,v] = find(jacobian(:,i_cols_j));
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nzA{i,j} = [i_rows(ir),i_cols_A(ic),v]';
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else
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d1 = dynamic_model(z,innovation,params,steady_state,i+1);
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end
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res(:,i,j) = d1;
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i_cols_Af = i_cols_Af + ny;
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end
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i_rows = i_rows + ny;
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if mod(j,nnodes) == 0
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i_w_p = i_w_p + 1;
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end
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if nargin > 1 && i > 1
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i_cols_As = i_cols_As + ny;
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end
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offset_r0 = offset_r0 + ny;
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end
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i_cols_p = i_cols_p + ny;
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i_cols_s = i_cols_s + ny;
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i_cols_f = i_cols_f + ny;
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end
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for j=1:world_nbr
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i_rows_y = i_cols+(order+1)*ny;
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offset_c = ny*(order+(nnodes-1)*(order-1)*order/2+j-1);
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offset_r = offset_r0+(j-1)*ny;
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for i=order+2:periods
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if nargin > 1
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[d1,jacobian] = dynamic_model(Y(i_rows_y,j),x,params, ...
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steady_state,i+1);
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if i < periods
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[ir,ic,v] = find(jacobian(:,i_cols_j));
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else
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[ir,ic,v] = find(jacobian(:,i_cols_T));
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end
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nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
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else
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d1 = dynamic_model(Y(i_rows_y,j),x,params, ...
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steady_state,i+1);
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end
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res(:,i,j) = d1;
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i_rows_y = i_rows_y + ny;
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offset_c = offset_c + world_nbr*ny;
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offset_r = offset_r + world_nbr*ny;
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end
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end
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if nargin > 1
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iA = [nzA{:}]';
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A = sparse(iA(:,1),iA(:,2),iA(:,3),dimension,dimension);
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end
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res = res(pm.i_upd_r);
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@ -1,4 +1,4 @@
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function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model_1(endo_simul,exo_simul,EpOptions,pfm,order,varargin)
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function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model_1(endo_simul,exo_simul,Options,pfm,order,varargin)
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% Copyright (C) 2012-2013 Dynare Team
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%
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@ -17,281 +17,135 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model_1(endo
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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if nargin < 6
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homotopy_parameter = 1;
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else
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homotopy_parameter = varargin{1};
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global options_
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if nargin < 6
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homotopy_parameter = 1;
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else
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homotopy_parameter = varargin{1};
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end
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flag = 0;
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err = 0;
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stop = 0;
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EpOptions = Options.ep;
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params = pfm.params;
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steady_state = pfm.steady_state;
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ny = pfm.ny;
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periods = pfm.periods;
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dynamic_model = pfm.dynamic_model;
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lead_lag_incidence = pfm.lead_lag_incidence;
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nyp = pfm.nyp;
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nyf = pfm.nyf;
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i_cols_1 = pfm.i_cols_1;
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i_cols_A1 = pfm.i_cols_A1;
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i_cols_j = pfm.i_cols_j;
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i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
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hybrid_order = pfm.hybrid_order;
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dr = pfm.dr;
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nodes = pfm.nodes;
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weights = pfm.weights;
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nnodes = pfm.nnodes;
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maxit = pfm.maxit_;
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tolerance = pfm.tolerance;
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verbose = pfm.verbose;
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number_of_shocks = size(exo_simul,2);
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% make sure that there is a node equal to zero
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% and permute nodes and weights to have zero first
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k = find(sum(abs(nodes),2) < 1e-12);
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if ~isempty(k)
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nodes = [nodes(k,:); nodes(1:k-1,:); nodes(k+1:end,:)];
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weights = [weights(k); weights(1:k-1); weights(k+1:end)];
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else
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error('there is no nodes equal to zero')
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end
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if hybrid_order > 0
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if hybrid_order == 2
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h_correction = 0.5*dr.ghs2(dr.inv_order_var);
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end
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flag = 0;
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err = 0;
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stop = 0;
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else
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h_correction = 0;
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end
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params = pfm.params;
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steady_state = pfm.steady_state;
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ny = pfm.ny;
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periods = pfm.periods;
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dynamic_model = pfm.dynamic_model;
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lead_lag_incidence = pfm.lead_lag_incidence;
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nyp = pfm.nyp;
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nyf = pfm.nyf;
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i_cols_1 = pfm.i_cols_1;
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i_cols_A1 = pfm.i_cols_A1;
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i_cols_j = pfm.i_cols_j;
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i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
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hybrid_order = pfm.hybrid_order;
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dr = pfm.dr;
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[nodes,weights,nnodes] = setup_integration_nodes(EpOptions,pfm);
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maxit = pfm.maxit_;
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tolerance = pfm.tolerance;
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verbose = pfm.verbose;
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if verbose
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disp ([' -----------------------------------------------------']);
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disp (['MODEL SIMULATION :']);
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fprintf('\n');
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end
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number_of_shocks = size(exo_simul,2);
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% Each column of Y represents a different world
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% The upper right cells are unused
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% The first row block is ny x 1
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% The second row block is ny x nnodes
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% The third row block is ny x nnodes^2
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% and so on until size ny x nnodes^order
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world_nbr = 1+(nnodes-1)*order;
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Y = endo_simul(:,2:end-1);
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Y = repmat(Y,1,world_nbr);
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pfm.y0 = endo_simul(:,1);
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% make sure that there is a node equal to zero
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% and permute nodes and weights to have zero first
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k = find(sum(abs(nodes),2) < 1e-12);
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if ~isempty(k)
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nodes = [nodes(k,:); nodes(1:k-1,:); nodes(k+1:end,:)];
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weights = [weights(k); weights(1:k-1); weights(k+1:end)];
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else
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error('there is no nodes equal to zero')
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end
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if hybrid_order > 0
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if hybrid_order == 2
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h_correction = 0.5*dr.ghs2(dr.inv_order_var);
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end
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% The columns of A map the elements of Y such that
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% each block of Y with ny rows are unfolded column wise
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% number of blocks
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block_nbr = (order+(nnodes-1)*(order-1)*order/2+(periods-order)*world_nbr);
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% dimension of the problem
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dimension = ny*block_nbr;
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pfm.block_nbr = block_nbr;
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pfm.dimension = dimension;
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if order == 0
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i_upd_r = (1:ny*periods);
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i_upd_y = i_upd_r + ny;
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else
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i_upd_r = zeros(dimension,1);
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i_upd_y = i_upd_r;
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i_upd_r(1:ny) = (1:ny);
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i_upd_y(1:ny) = ny+(1:ny);
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i1 = ny+1;
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i2 = 2*ny;
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n1 = ny+1;
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n2 = 2*ny;
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for i=2:periods
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k = n1:n2;
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for j=1:(1+(nnodes-1)*min(i-1,order))
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i_upd_r(i1:i2) = k+(j-1)*ny*periods;
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i_upd_y(i1:i2) = k+ny+(j-1)*ny*(periods+2);
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i1 = i2+1;
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i2 = i2+ny;
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end
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n1 = n2+1;
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n2 = n2+ny;
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end
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end
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icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
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find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
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h1 = clock;
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pfm.order = order;
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pfm.world_nbr = world_nbr;
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pfm.nodes = nodes;
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pfm.nnodes = nnodes;
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pfm.weights = weights;
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pfm.h_correction = h_correction;
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pfm.i_rows = 1:ny;
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i_cols = find(lead_lag_incidence');
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pfm.i_cols = i_cols;
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pfm.nyp = nyp;
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pfm.nyf = nyf;
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pfm.hybrid_order = hybrid_order;
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pfm.i_cols_1 = i_cols_1;
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pfm.i_cols_h = i_cols_j;
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pfm.icA = icA;
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pfm.i_cols_T = i_cols_T;
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pfm.i_upd_r = i_upd_r;
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pfm.i_upd_y = i_upd_y;
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if verbose
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disp ([' -----------------------------------------------------']);
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disp (['MODEL SIMULATION :']);
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fprintf('\n');
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end
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z = endo_simul(find(lead_lag_incidence'));
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[d1,jacobian] = dynamic_model(z,exo_simul,params,steady_state,2);
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% Each column of Y represents a different world
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% The upper right cells are unused
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% The first row block is ny x 1
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% The second row block is ny x nnodes
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% The third row block is ny x nnodes^2
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% and so on until size ny x nnodes^order
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world_nbr = 1+(nnodes-1)*order;
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Y = repmat(endo_simul(:),1,world_nbr);
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% The columns of A map the elements of Y such that
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% each block of Y with ny rows are unfolded column wise
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dimension = ny*(order+(nnodes-1)*(order-1)*order/2+(periods-order)*world_nbr);
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if order == 0
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i_upd_r = (1:ny*periods);
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i_upd_y = i_upd_r + ny;
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else
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i_upd_r = zeros(dimension,1);
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i_upd_y = i_upd_r;
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i_upd_r(1:ny) = (1:ny);
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i_upd_y(1:ny) = ny+(1:ny);
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i1 = ny+1;
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i2 = 2*ny;
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n1 = ny+1;
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n2 = 2*ny;
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for i=2:periods
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k = n1:n2;
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for j=1:(1+(nnodes-1)*min(i-1,order))
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i_upd_r(i1:i2) = k+(j-1)*ny*periods;
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i_upd_y(i1:i2) = k+ny+(j-1)*ny*(periods+2);
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i1 = i2+1;
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i2 = i2+ny;
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end
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n1 = n2+1;
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n2 = n2+ny;
|
||||
end
|
||||
end
|
||||
icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
|
||||
find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
|
||||
h1 = clock;
|
||||
for iter = 1:maxit
|
||||
h2 = clock;
|
||||
A1 = sparse([],[],[],ny*(order+(nnodes-1)*(order-1)*order/2),dimension,(order+1)*world_nbr*nnz(jacobian));
|
||||
res = zeros(ny,periods,world_nbr);
|
||||
i_rows = 1:ny;
|
||||
i_cols = find(lead_lag_incidence');
|
||||
i_cols_p = i_cols(1:nyp);
|
||||
i_cols_s = i_cols(nyp+(1:ny));
|
||||
i_cols_f = i_cols(nyp+ny+(1:nyf));
|
||||
i_cols_A = i_cols;
|
||||
i_cols_Ap0 = i_cols_p;
|
||||
i_cols_As = i_cols_s;
|
||||
i_cols_Af0 = i_cols_f - ny;
|
||||
i_hc = i_cols_f - 2*ny;
|
||||
for i = 1:order+1
|
||||
i_w_p = 1;
|
||||
for j = 1:(1+(nnodes-1)*(i-1))
|
||||
innovation = exo_simul;
|
||||
if i <= order && j == 1
|
||||
% first world, integrating future shocks
|
||||
for k=1:nnodes
|
||||
if i == 2
|
||||
i_cols_Ap = i_cols_Ap0;
|
||||
elseif i > 2
|
||||
i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes- ...
|
||||
1)*(i-2)*(i-3)/2);
|
||||
end
|
||||
if k == 1
|
||||
i_cols_Af = i_cols_Af0 + ny*(i-1+(nnodes-1)*i*(i-1)/ ...
|
||||
2);
|
||||
else
|
||||
i_cols_Af = i_cols_Af0 + ny*(i-1+(nnodes-1)*i*(i-1)/ ...
|
||||
2+(i-1)*(nnodes-1) ...
|
||||
+ k - 1);
|
||||
end
|
||||
if i > 1
|
||||
innovation(i+1,:) = nodes(k,:);
|
||||
end
|
||||
if k == 1
|
||||
k1 = 1;
|
||||
else
|
||||
k1 = (nnodes-1)*(i-1)+k;
|
||||
end
|
||||
if hybrid_order == 2 && (k > 1 || i == order)
|
||||
y = [Y(i_cols_p,1);
|
||||
Y(i_cols_s,1);
|
||||
Y(i_cols_f,k1)+h_correction(i_hc)];
|
||||
else
|
||||
y = [Y(i_cols_p,1);
|
||||
Y(i_cols_s,1);
|
||||
Y(i_cols_f,k1)];
|
||||
end
|
||||
[d1,jacobian] = dynamic_model(y,homotopy_parameter*innovation, ...
|
||||
params,steady_state,i+1);
|
||||
if i == 1
|
||||
% in first period we don't keep track of
|
||||
% predetermined variables
|
||||
i_cols_A = [i_cols_As - ny; i_cols_Af];
|
||||
A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
|
||||
else
|
||||
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
|
||||
A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
|
||||
end
|
||||
res(:,i,1) = res(:,i,1)+weights(k)*d1;
|
||||
end
|
||||
elseif j > 1 + (nnodes-1)*(i-2)
|
||||
% new world, using previous state of world 1 and hit
|
||||
% by shocks from nodes
|
||||
i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes-1)*(i-2)*(i-3)/2);
|
||||
i_cols_Af = i_cols_Af0 + ny*(i+(nnodes-1)*i*(i-1)/2+j-2);
|
||||
k = j - (nnodes-1)*(i-2);
|
||||
innovation(i+1,:) = nodes(k,:);
|
||||
y = [Y(i_cols_p,1);
|
||||
Y(i_cols_s,j);
|
||||
Y(i_cols_f,j)];
|
||||
[d1,jacobian] = dynamic_model(y,homotopy_parameter*innovation,params,steady_state,i+1);
|
||||
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
|
||||
A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
|
||||
res(:,i,j) = d1;
|
||||
i_cols_Af = i_cols_Af + ny;
|
||||
else
|
||||
% existing worlds other than 1
|
||||
i_cols_Ap = i_cols_Ap0 + ny*(i-2+(nnodes-1)*(i-2)*(i-3)/2+j-1);
|
||||
i_cols_Af = i_cols_Af0 + ny*(i+(nnodes-1)*i*(i-1)/2+j-2);
|
||||
y = [Y(i_cols_p,j);
|
||||
Y(i_cols_s,j);
|
||||
Y(i_cols_f,j)];
|
||||
[d1,jacobian] = dynamic_model(y,homotopy_parameter*innovation,params,steady_state,i+1);
|
||||
i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
|
||||
A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
|
||||
res(:,i,j) = d1;
|
||||
i_cols_Af = i_cols_Af + ny;
|
||||
end
|
||||
i_rows = i_rows + ny;
|
||||
if mod(j,nnodes) == 0
|
||||
i_w_p = i_w_p + 1;
|
||||
end
|
||||
if i > 1
|
||||
i_cols_As = i_cols_As + ny;
|
||||
end
|
||||
end
|
||||
i_cols_p = i_cols_p + ny;
|
||||
i_cols_s = i_cols_s + ny;
|
||||
i_cols_f = i_cols_f + ny;
|
||||
end
|
||||
nzA = cell(periods,world_nbr);
|
||||
parfor j=1:world_nbr
|
||||
i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
|
||||
offset_c = ny*(order+(nnodes-1)*(order-1)*order/2+j-1);
|
||||
offset_r = (j-1)*ny;
|
||||
for i=order+2:periods
|
||||
[d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
|
||||
exo_simul,params, ...
|
||||
steady_state,i+1);
|
||||
if i == periods
|
||||
[ir,ic,v] = find(jacobian(:,i_cols_T));
|
||||
else
|
||||
[ir,ic,v] = find(jacobian(:,i_cols_j));
|
||||
end
|
||||
nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
|
||||
res(:,i,j) = d1;
|
||||
i_rows_y = i_rows_y + ny;
|
||||
offset_c = offset_c + world_nbr*ny;
|
||||
offset_r = offset_r + world_nbr*ny;
|
||||
end
|
||||
end
|
||||
err = max(abs(res(i_upd_r)));
|
||||
if verbose
|
||||
[err1, k1] = max(abs(res));
|
||||
[err2, k2] = max(abs(err1));
|
||||
[err3, k3] = max(abs(err2));
|
||||
disp([iter err k1(:,k2(:,:,k3),k3) k2(:,:,k3) k3])
|
||||
end
|
||||
if err < tolerance
|
||||
stop = 1;
|
||||
flag = 0;% Convergency obtained.
|
||||
endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
|
||||
if verbose
|
||||
save ep_test_s1 exo_simul endo_simul Y
|
||||
fprintf('\n') ;
|
||||
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
|
||||
fprintf('\n') ;
|
||||
disp([' Convergency obtained.']) ;
|
||||
fprintf('\n') ;
|
||||
end
|
||||
break
|
||||
end
|
||||
A2 = [nzA{:}]';
|
||||
if any(isnan(A2(:,3))) || any(any(any(isnan(res))))
|
||||
if verbose
|
||||
disp(['solve_stochastic_foresight_model_1 encountered ' ...
|
||||
'NaN'])
|
||||
save ep_test_s2 exo_simul endo_simul
|
||||
pause
|
||||
end
|
||||
flag = 1;
|
||||
return
|
||||
end
|
||||
A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
|
||||
if verbose
|
||||
disp(sprintf('condest %g',condest(A)))
|
||||
end
|
||||
dy = -A\res(i_upd_r);
|
||||
Y(i_upd_y) = Y(i_upd_y) + dy;
|
||||
end
|
||||
|
||||
if ~stop
|
||||
if verbose
|
||||
fprintf('\n') ;
|
||||
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
|
||||
fprintf('\n') ;
|
||||
disp(['WARNING : maximum number of iterations is reached (modify options_.simul.maxit).']) ;
|
||||
fprintf('\n') ;
|
||||
disp(sprintf('err: %f',err));
|
||||
save ep_test_s2 exo_simul endo_simul
|
||||
pause
|
||||
end
|
||||
flag = 1;% more iterations are needed.
|
||||
endo_simul = 1;
|
||||
end
|
||||
if verbose
|
||||
disp (['-----------------------------------------------------']) ;
|
||||
end
|
||||
options_.solve_algo = 9;
|
||||
options_.steady.maxit = 100;
|
||||
y = repmat(steady_state,block_nbr,1);
|
||||
y = dynare_solve(@ep_problem_2,y,1,exo_simul,pfm);
|
||||
endo_simul(:,2) = y(1:ny);
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
function [X,w]=stroud_judd_7.5.8(d)
|
||||
|
||||
E = eye(d);
|
||||
X = cell(2*d,1);
|
||||
m = 1;
|
||||
for i=1:d
|
||||
X{m} = E(:,i);
|
||||
m = m+1;
|
||||
X{m} = -E(:,i);
|
||||
Loading…
Reference in New Issue