103 lines
2.8 KiB
Matlab
103 lines
2.8 KiB
Matlab
function y = var_forecast(name, h, y, fcv)
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% name : filename
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% name string name of var model, provided in var statement
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% h int number of steps-ahead forecast
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% y matrix rows: realizations of endogenous variables in declaration order; cols: realizations in t, t-1, t-2 ... order of VAR
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% fcv string name of variable we want forecast for
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% returns the h-step-ahead VAR(order) forecast for fcv
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% example calling:
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% In Matlab:
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% >> autoregressive_matrices{1} = [0.5000 0.1000; 0.4000 0.5000];
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% >> autoregressive_matrices{2} = [0 0 ; 0.2500 0 ];
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% >> mu = [0.0200; 0.0300];
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% >> save('m1.mat', 'mu','autoregressive_matrices');
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% In .mod file:
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% var a b c d;
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% ...
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% var(model_name=m1,order=2) a c;
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% From Matlab backend:
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% >> yt = [0.0600; 33.0000; 0.0300; 22.0000];
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% >> ytm1 = [0.0550; 11.0000; 0.0300; 88.0000];
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% >> var_forecast('m1', 1, [yt ytm1])
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% >> var_forecast('m1', 2, [yt ytm1], ['a'])
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%%
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global M_;
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%% construct y
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assert( ...
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length(y) == length(M_.endo_names) || ... % when called from static model
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length(y) == sum(sum(M_.lead_lag_incidence ~= 0)) ... % when called from dynamic model
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);
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yidx = zeros(size(M_.endo_names));
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for i=1:size(M_.var.(name).var_list_,1)
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yidx = yidx | strcmp(strtrim(M_.var.(name).var_list_(i,:)), M_.endo_names);
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end
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y = y(yidx,:);
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if nargin == 4
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fvidx = strcmp(fcv, M_.endo_names);
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end
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%% load .mat file
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load(name, 'autoregressive_matrices', 'mu');
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if ~exist('autoregressive_matrices', 'var') || ~exist('mu', 'var')
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error([name ' : must contain the variables autoregressive_matrices and mu']);
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end
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assert(h >= 1);
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%% rewrite as VAR(1)
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lm = length(mu);
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lc = length(autoregressive_matrices);
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assert(lc == M_.var.(name).order);
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if size(y,1) ~= lm || size(y,2) ~= M_.var.(name).order
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error('The dimensions of y are not correct. It should be an nvars x order matrix');
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end
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A = zeros(lm*lc, lm*lc);
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for i=1:lc
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if any([lm lm] ~= size(autoregressive_matrices{i}))
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error('The dimensions of mu and autoregressive_matrices are off');
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end
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col = lm*(i-1)+1:lm*i;
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A(1:lm, col) = autoregressive_matrices{i};
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if i ~= lc
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A(lm*i+1:lm*i+lm, col) = eye(lm, lm);
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end
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end
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if M_.var.(name).order > 1
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mu = [mu; zeros(lm*M_.var.(name).order-lm, 1)];
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end
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%% Calculate Forecast
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% New Introduction to Multiple Time Series Analysis
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% Helmut Lutkepohl
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% page 34
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%
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% An = eye(size(A));
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% for i=1:h-1
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% An = An + A^i;
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% end
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% y = An*mu + A^h*y(:);
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for i=1:h
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y = mu + A*y(:);
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end
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y = y(1:lm);
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if nargin == 4
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retidx = find(fvidx & yidx == 1);
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if isempty(retidx)
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return;
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elseif retidx == 1
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y = y(1);
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else
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y = y(sum(yidx(1:retidx-1))+1);
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end
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end
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end |