119 lines
2.8 KiB
Matlab
119 lines
2.8 KiB
Matlab
function icforecast(ptype,cV,cS,cL,H,mcValue,B,ci)
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% stephane.adjemian@ens.fr
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global options_ oo_ M_
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xparam = get_posterior_parameters(ptype);
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gend = options_.nobs;
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% Read and demean data
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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if options_.loglinear == 1 & ~options_.logdata
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rawdata = log(rawdata);
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end
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if options_.prefilter == 1
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bayestopt_.mean_varobs = mean(rawdata,1);
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data = transpose(rawdata-ones(gend,1)*bayestopt_.mean_varobs);
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else
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data = transpose(rawdata);
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end
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set_parameters(xparam);
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[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data);
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InitState(:,1) = atT(:,end);
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[T,R,ys,info] = dynare_resolve;
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sQ = sqrt(M_.Sigma_e);
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NumberOfStates = length(InitState);
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FORCS1 = zeros(NumberOfStates,H+1,B);
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for b=1:B
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FORCS1(:,1,b) = InitState;
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end
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EndoSize = size(M_.endo_names,1);
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ExoSize = size(M_.exo_names,1);
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n1 = size(cV,1);
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n2 = size(cS,1);
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if n1 ~= n2
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disp('imcforecast :: Error!')
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disp(['imcforecast :: The number of variables doesn''t match the number of shocks'])
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return
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end
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idx = [];
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jdx = [];
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for i = 1:n1
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idx = [idx ; oo_.dr.inv_order_var(strmatch(deblank(cV(i,:)),M_.endo_names,'exact'))];
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jdx = [jdx ; strmatch(deblank(cS(i,:)),M_.exo_names,'exact')];
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end
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mv = zeros(n1,NumberOfStates);
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mu = zeros(ExoSize,n2);
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for i=1:n1
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mv(i,idx(i)) = 1;
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mu(jdx(i),i) = 1;
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end
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if (size(mcValue,2) == 1);
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mcValue = mcValue*ones(1,cL);
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else
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cL = size(mcValue,2);
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end
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randn('state',0);
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for b=1:B
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shocks = sQ*randn(ExoSize,H);
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shocks(jdx,:) = zeros(length(jdx),H);
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FORCS1(:,:,b) = mcforecast3(cL,H,mcValue,shocks,FORCS1(:,:,b),T,R,mv, mu);
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end
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mFORCS1 = mean(FORCS1,3);
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tt = (1-ci)/2;
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t1 = round(B*tt);
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t2 = round(B*(1-tt));
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forecasts.controled_variables = cV;
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forecasts.instruments = cS;
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for i = 1:EndoSize
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eval(['forecasts.cond.mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS1(i,:)'';']);
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tmp = sort(squeeze(FORCS1(i,:,:))');
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eval(['forecasts.cond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
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' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
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end
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clear FORCS1;
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FORCS2 = zeros(NumberOfStates,H+1,B);
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for b=1:B
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FORCS2(:,1,b) = InitState;
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end
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randn('state',0);
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for b=1:B
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shocks = sQ*randn(ExoSize,H);
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shocks(jdx,:) = zeros(length(jdx),H);
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FORCS2(:,:,b) = mcforecast3(0,H,mcValue,shocks,FORCS2(:,:,b),T,R,mv, mu);
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end
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mFORCS2 = mean(FORCS2,3);
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for i = 1:EndoSize
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eval(['forecasts.uncond.mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS2(i,:)'';']);
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tmp = sort(squeeze(FORCS2(i,:,:))');
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eval(['forecasts.uncond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
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' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
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end
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save('conditional_forecasts.mat','forecasts'); |