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Update sur and surgibbs function outputs.

time-shift
Dóra Kocsis 2019-11-20 16:53:53 +01:00
parent 7b1c61f63c
commit 75a929051f
7 changed files with 72 additions and 21 deletions
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8
matlab/ols/sur.m
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@ -98,7 +98,7 @@ ast = handle_constant_eqs(get_ast(eqtags));
neqs = length(ast);
%% Find parameters and variable names in equations and setup estimation matrices
[Y, lhssub, X, ~, ~, residnames] = common_parsing(ds(ds_range), ast, true, param_names);
[Y, lhssub, X, fp, lp, residnames] = common_parsing(ds(ds_range), ast, true, param_names);
clear ast
nobs = Y{1}.nobs;
[Y, lhssub, X, constrained] = put_in_sur_form(Y, lhssub, X);
@ -114,6 +114,9 @@ if ~isempty(st) && strcmp(st(1).name, 'surgibbs')
varargout{2} = X{param_names{:}}.data;
varargout{3} = Y.data;
varargout{4} = neqs;
varargout{5} = lhssub.data;
varargout{6} = fp{1};
varargout{7} = lp{1};
return
end
@ -176,6 +179,9 @@ oo_.sur.(model_name).resid = Y.data - oo_.sur.(model_name).Yhat;
% Correct Yhat reported back to user
oo_.sur.(model_name).Yhat = oo_.sur.(model_name).Yhat + lhssub;
yhatname = [model_name '_FIT'];
ds.(yhatname) = dseries(oo_.sur.(model_name).Yhat.data, fp{1}, yhatname);
varargout{1} = ds;
%% Calculate statistics
% Estimate for sigma^2

75
matlab/surgibbs.m
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@ -1,5 +1,5 @@
function surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
%function surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
function ds = surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
%function ds = surgibbs(ds, param_names, beta0, A, ndraws, discarddraws, thin, eqtags, model_name)
% Implements Gibbs Samipling for SUR
%
% INPUTS
@ -88,11 +88,13 @@ end
% Using a Combination of Direct Monte Carlo and Importance Sampling
% Techniques. Bayesian Analysis. 2010. pp 67-70.
if nargin == 8
[nobs, X, Y, m] = sur(ds, param_names, eqtags);
[nobs, X, Y, m, lhssub, fp, ~] = sur(ds, param_names, eqtags);
else
[nobs, X, Y, m] = sur(ds, param_names);
[nobs, X, Y, m, lhssub, fp, ~] = sur(ds, param_names);
end
oo_.surgibbs.(model_name).dof = nobs;
beta = beta0;
A = inv(A);
thinidx = 1;
@ -116,7 +118,7 @@ for i = 1:ndraws
beta = rand_multivariate_normal(betabar', chol(Omegabar), nparams)';
if i > discarddraws
if thinidx == thin
oo_.surgibbs.(model_name).betadraws(drawidx, :) = beta';
oo_.surgibbs.(model_name).betadraws(drawidx, 1:nparams) = beta';
thinidx = 1;
drawidx = drawidx + 1;
else
@ -125,22 +127,65 @@ for i = 1:ndraws
end
end
% save parameter values
oo_.surgibbs.(model_name).beta = (sum(oo_.surgibbs.(model_name).betadraws)/rows(oo_.surgibbs.(model_name).betadraws))';
%% Save posterior moments.
oo_.surgibbs.(model_name).posterior.mean.beta = (sum(oo_.surgibbs.(model_name).betadraws)/rows(oo_.surgibbs.(model_name).betadraws))';
oo_.surgibbs.(model_name).posterior.variance.beta = cov(oo_.surgibbs.(model_name).betadraws);
incidxs = zeros(length(param_names), 1);
for i = 1:length(param_names)
incidxs(i) = strmatch(param_names{i}, M_.param_names, 'exact');
M_.params(incidxs(i)) = oo_.surgibbs.(model_name).beta(i);
% Yhat
oo_.surgibbs.(model_name).Yhat = X*oo_.surgibbs.(model_name).posterior.mean.beta;
% Residuals
oo_.surgibbs.(model_name).resid = Y - oo_.surgibbs.(model_name).Yhat;
% Correct Yhat reported back to user
oo_.surgibbs.(model_name).Yhat = oo_.surgibbs.(model_name).Yhat + lhssub;
yhatname = [model_name '_FIT'];
ds.(yhatname) = dseries(oo_.surgibbs.(model_name).Yhat, fp, yhatname);
% Compute and save posterior densities.
for i=1:nparams
xx = oo_.surgibbs.(model_name).betadraws(:,i);
nn = length(xx);
bandwidth = mh_optimal_bandwidth(xx, nn, 0, 'gaussian');
[x, f] = kernel_density_estimate(xx, 512, nn, bandwidth, 'gaussian');
oo_.surgibbs.(model_name).posterior.density.(param_names{i}) = [x, f];
end
% Update model1s parameters with posterior mean.
oo_.surgibbs.(model_name).param_idxs = zeros(length(param_names), 1);
for i = 1:length(param_names)
if ~strcmp(param_names{i}, 'intercept')
oo_.surgibbs.(model_name).param_idxs(i) = find(strcmp(M_.param_names, param_names{i}));
M_.params(oo_.surgibbs.(model_name).param_idxs(i)) = oo_.surgibbs.(model_name).posterior.mean.beta(i);
end
end
oo_.surgibbs.(model_name).pnames = param_names;
oo_.surgibbs.(model_name).neqs = m;
% Estimate for sigma^2
SS_res = oo_.surgibbs.(model_name).resid'*oo_.surgibbs.(model_name).resid;
oo_.surgibbs.(model_name).s2 = SS_res/oo_.surgibbs.(model_name).dof;
% R^2
ym = Y - mean(Y);
SS_tot = ym'*ym;
oo_.surgibbs.(model_name).R2 = 1 - SS_res/SS_tot;
% Write .inc file
write_param_init_inc_file('surgibbs', model_name, incidxs, oo_.surgibbs.(model_name).beta);
write_param_init_inc_file('surgibbs', model_name, oo_.surgibbs.(model_name).param_idxs, oo_.surgibbs.(model_name).posterior.mean.beta);
%% Print Output
if ~options_.noprint
dyn_table('Gibbs Sampling on SUR', {}, {}, param_names, ...
{'Parameter Value'}, 4, oo_.surgibbs.(model_name).beta);
ttitle = ['Gibbs Sampling on SUR'];
preamble = {['Model name: ' model_name], ...
sprintf('No. Equations: %d', oo_.surgibbs.(model_name).neqs), ...
sprintf('No. Independent Variables: %d', size(X, 2)), ...
sprintf('Observations: %d', oo_.surgibbs.(model_name).dof)};
afterward = {sprintf('s^2: %f', oo_.surgibbs.(model_name).s2), sprintf('R^2: %f', oo_.surgibbs.(model_name).R2)};
dyn_table(ttitle, preamble, afterward, param_names,...
{'Posterior mean', 'Posterior std.'}, 4,...
[oo_.surgibbs.(model_name).posterior.mean.beta, sqrt(diag(oo_.surgibbs.(model_name).posterior.variance.beta))]);
end
%% Plot
@ -155,7 +200,7 @@ if ~options_.nograph
subplot(nrows, ncols, j)
histogram(oo_.surgibbs.(model_name).betadraws(:, j))
hc = histcounts(oo_.surgibbs.(model_name).betadraws(:, j));
line([oo_.surgibbs.(model_name).beta(j) oo_.surgibbs.(model_name).beta(j)], [min(hc) max(hc)], 'Color', 'red');
line([oo_.surgibbs.(model_name).posterior.mean.beta(j) oo_.surgibbs.(model_name).posterior.mean.beta(j)], [min(hc) max(hc)], 'Color', 'red');
title(param_names{j}, 'Interpreter', 'none')
end
end

2
tests/ecb/SUR/panel_var_diff_NB_simulation_test.mod
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@ -172,7 +172,7 @@ for i=1:NSIMS
idxs = [idxs j];
end
end
sur(simdata{idxs});
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
end

2
tests/ecb/SUR/panel_var_diff_NB_simulation_zero_eq.mod
View File

@ -171,7 +171,7 @@ for i=1:NSIMS
idxs = [idxs j];
end
end
sur(simdata{idxs});
simdata = sur(simdata{idxs});
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
end

2
tests/ecb/SUR/sur_noniterative.mod
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@ -172,7 +172,7 @@ for i=1:NSIMS
idxs = [idxs j];
end
end
sur(simdata{idxs}, {}, {}, 'mymodel', true);
simdata = sur(simdata{idxs}, {}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
end

2
tests/ecb/SUR/sur_params_noniterative.mod
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@ -172,7 +172,7 @@ for i=1:NSIMS
idxs = [idxs j];
end
end
sur(simdata{idxs}, {'u2_q_yed_u2_g_yer_L1', 'u2_estn_u2_estn_L1', 'u2_ehic_u2_ehic_L1', 'de_q_yed_de_g_yer_L1', 'u2_g_yer_u2_g_yer_L1', 'u2_stn_u2_q_yed_L1', 'u2_q_yed_ecm_u2_q_yed_L1', 'de_g_yer_u2_stn_L1'}, {}, 'mymodel', true);
simdata = sur(simdata{idxs}, {'u2_q_yed_u2_g_yer_L1', 'u2_estn_u2_estn_L1', 'u2_ehic_u2_ehic_L1', 'de_q_yed_de_g_yer_L1', 'u2_g_yer_u2_g_yer_L1', 'u2_stn_u2_q_yed_L1', 'u2_q_yed_ecm_u2_q_yed_L1', 'de_g_yer_u2_stn_L1'}, {}, 'mymodel', true);
BETA(i, :) = M_.params';
oo_ = rmfield(oo_, 'sur');
end

2
tests/ecb/SURGibbs/fulton_fish.mod
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@ -30,7 +30,7 @@ estparams = {'bq1' 'bq0'};
estparamsval = [bq1 bq0];
A = 0.0005.*eye(length(estparams));
surgibbs(dseries('fishdata.csv'), estparams, estparamsval, A, 20000, 5000, 7);
simdata = surgibbs(dseries('fishdata.csv'), estparams, estparamsval, A, 20000, 5000, 7);
good = [6.791587808530124
8.552700000000000