function writeVarExpectationFunction(var_model_name, horizon) %function writeVarExpectationFunction(model_name) %% global M_; %% open file basename = ['var_forecast_' var_model_name]; fid = fopen([basename '.m'], 'w'); if fid == -1 error(['Could not open ' basename '.m for writing']); end %% load .mat file load(var_model_name, 'autoregressive_matrices', 'mu'); if ~exist('autoregressive_matrices', 'var') || ~exist('mu', 'var') error([var_model_name '.mat : must contain the variables autoregressive_matrices and mu']); end %% fprintf(fid, 'function ret = %s(y)\n', basename); fprintf(fid, '%%function ret = %s(y)\n', basename); fprintf(fid, '%% Calculates the %d-step-ahead forecast from the VAR model %s\n', max(horizon), var_model_name); fprintf(fid, '%%\n%% Created automatically by Dynare on %s\n%%\n\n', datetime); fprintf(fid, '%%%% Construct y\n'); fprintf(fid, 'assert(length(y) == %d);\n', sum(sum(M_.lead_lag_incidence ~= 0))); endo_names = cellstr(M_.endo_names); nvars = length(M_.var.(var_model_name).var_list_); var_model_order = M_.var.(var_model_name).order; yidx = zeros(nvars, min(var_model_order, 2)); % first for order <= 2, drawing variables directly from their endo_names for i=1:min(var_model_order, 2) if mod(i, 2) == 0 ridx = 1; else ridx = 2; end for j=1:nvars yidx(j, i) = M_.lead_lag_incidence(ridx, strcmp(strtrim(M_.var.(var_model_name).var_list_(j,:)), endo_names)'); end end yidx = yidx(:); % then for order > 2 if var_model_order > 2 y1idx = zeros((var_model_order - 2)*nvars, var_model_order - 2); for i=3:var_model_order for j=1:nvars varidx = [M_.aux_vars.orig_index] == find(strcmp(strtrim(M_.var.(var_model_name).var_list_(j,:)), endo_names)) ... & [M_.aux_vars.orig_lead_lag] == -i; cidx = [M_.aux_vars.endo_index]; cidx = cidx(varidx); y1idx(j, i-2) = M_.lead_lag_incidence(2, cidx); end end yidx = [yidx ; y1idx(:)]; end fprintf(fid, 'y = y(['); fprintf(fid, '%d ', yidx); fprintf(fid, ']);\n'); lm = length(mu); lc = length(autoregressive_matrices); assert(lc == var_model_order); A = zeros(lm*lc, lm*lc); for i=1:lc if any([lm lm] ~= size(autoregressive_matrices{i})) error(['The dimensions of mu and autoregressive_matrices for ' var_model_name ' are off']); end col = lm*(i-1)+1:lm*i; A(1:lm, col) = autoregressive_matrices{i}; if i ~= lc A(lm*i+1:lm*i+lm, col) = eye(lm, lm); end end if var_model_order > 1 mu = [mu; zeros(lm*var_model_order-lm, 1)]; end fprintf(fid, '\n%%%% Calculate %d-step-ahead forecast for VAR(%d) written as VAR(1)\n', max(horizon), var_model_order); fprintf(fid, '%% Follows Lütkepohl (2005) pg 15 & 34\n'); if max(horizon) == 1 printInsideOfLoop(fid, mu, A, false); fprintf(fid, 'ret(1, :) = y(1:%d);\n', lm); else fprintf(fid, 'retidx = 1;\n'); fprintf(fid, 'ret = zeros(%d, %d);\n', length(horizon), lm); fprintf(fid, 'for i=1:%d\n', max(horizon)); printInsideOfLoop(fid, mu, A, true); if length(horizon) == 1 fprintf(fid, ' if %d == i\n', horizon); else fprintf(fid, ' if any(['); fprintf(fid, '%d ', horizon); fprintf(fid, '] == i)\n'); end fprintf(fid, ' ret(retidx, :) = y(1:%d);\n', lm); fprintf(fid, ' retidx = retidx + 1;\n'); % fprintf(fid, ' ret(['); % fprintf(fid, '%d ', horizon); % fprintf(fid, '] == i, :) = y(1:%d);\n', lm); fprintf(fid, ' end\n'); fprintf(fid, 'end\n'); end %% close file fprintf(fid, 'end\n'); fclose(fid); end function printInsideOfLoop(fid, mu, A, inloop) if inloop fs = ' '; ns = ' '; spaces = ' '; else fs = ''; ns = ' '; spaces = ' '; end fprintf(fid, '%sy = ...\n%s[ ... %% intercept\n%s', fs, spaces, ns); fprintf(fid, [repmat('% f ', 1, size(mu, 2)) '; ...\n' ns], mu'); fprintf(fid, ' ] + ...\n%s[ ... %% autoregressive matrices\n%s', spaces, ns); fprintf(fid, [repmat('% f ', 1, size(A, 2)) '; ...\n' ns], A'); fprintf(fid, ' ] * y;\n'); end