/usr/share/dynare/matlab/@dseries/hptrend.m is in dynare-common 4.4.1-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | function ts = hptrend(ts, lambda) % --*-- Unitary tests --*--
% ts = hptrend(ts, lambda)
%
% Extracts the trend component from a dseries object using Hodrick Prescott filter.
%
% INPUTS
% o ts dseries object.
% o lambda positive scalar, trend smoothness parameter.
%
% OUTPUTS
% o ts dseries object, with time series replaced by the trend component of the original time series.
% Copyright (C) 2013 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
if nargin>1
if lambda<=0
error(['dseries::hptrend: Lambda must be a positive integer!'])
end
else
lambda = [];
end
for i=1:ts.vobs
ts.name(i) = {['hptrend(' ts.name{i} ')']};
ts.tex(i) = {['\text{hptrend}(' ts.tex{i} ')']};
end
ts.data = sample_hp_filter(ts.data,lambda);
%@test:1
%$ plot_flag = 0;
%$
%$ % Create a dataset.
%$ e = .2*randn(200,1);
%$ u = randn(200,1);
%$ stochastic_trend = cumsum(e);
%$ deterministic_trend = .1*transpose(1:200);
%$ x = zeros(200,1);
%$ for i=2:200
%$ x(i) = .75*x(i-1) + e(i);
%$ end
%$ y = x + stochastic_trend + deterministic_trend;
%$
%$ % Test the routine.
%$ try
%$ ts0 = dseries(y,'1950Q1');
%$ ts1 = dseries(stochastic_trend+deterministic_trend,'1950Q1');
%$ ts2 = ts0.hptrend(1600);
%$ t(1) = 1;
%$ catch
%$ t(1) = 0;
%$ end
%$
%$ if t(1)
%$ t(2) = dyn_assert(ts2.freq,4);
%$ t(3) = dyn_assert(ts2.init.freq,4);
%$ t(4) = dyn_assert(ts2.init.time,[1950, 1]);
%$ t(5) = dyn_assert(ts2.vobs,1);
%$ t(6) = dyn_assert(ts2.nobs,200);
%$ end
%$
%$ % Show results
%$ if plot_flag
%$ plot(ts1.data,'-k'); % Plot of the stationary component.
%$ hold on
%$ plot(ts2.data,'--r'); % Plot of the filtered y.
%$ hold off
%$ axis tight
%$ id = get(gca,'XTick');
%$ set(gca,'XTickLabel',strings(ts1.dates(id)));
%$ legend({'Nonstationary component of y', 'Estimated trend of y'})
%$ print('-depsc2','../doc/dynare.plots/HPTrend.eps')
%$ system('convert -density 300 ../doc/dynare.plots/HPTrend.eps ../doc/dynare.plots/HPTrend.png');
%$ system('convert -density 300 ../doc/dynare.plots/HPTrend.eps ../doc/dynare.plots/HPTrend.pdf');
%$ system('convert -density 300 ../doc/dynare.plots/HPTrend.eps ../doc/dynare.plots/HPTrend.jpg');
%$ end
%$ T = all(t);
%@eof:1
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