88 lines
2.2 KiB
Matlab
88 lines
2.2 KiB
Matlab
|
function ts = hpcycle(ts, lambda) % --*-- Unitary tests --*--
|
||
|
|
||
|
% ts = hpcycle(ts, lambda)
|
||
|
%
|
||
|
% Extracts the cycle component form 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 cyclical 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::hpcycle: Lambda must be a positive integer!'])
|
||
|
end
|
||
|
else
|
||
|
lambda = [];
|
||
|
end
|
||
|
|
||
|
for i=1:ts.vobs
|
||
|
ts.name(i) = {['hpcycle(' ts.name{i} ')']};
|
||
|
ts.tex(i) = {['\text{hpcycle}(' ts.tex{i} ')']};
|
||
|
end
|
||
|
|
||
|
[junk, data] = sample_hp_filter(ts.data,lambda);
|
||
|
ts.data = data;
|
||
|
|
||
|
%@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) = .9*x(i-1) + e(i);
|
||
|
%$ end
|
||
|
%$ y = x + stochastic_trend + deterministic_trend;
|
||
|
%$
|
||
|
%$ % Test the routine.
|
||
|
%$ try
|
||
|
%$ ts = dseries(y,'1950Q1');
|
||
|
%$ ts = ts.hpcycle();
|
||
|
%$ t(1) = 1;
|
||
|
%$ catch
|
||
|
%$ t(1) = 0;
|
||
|
%$ end
|
||
|
%$
|
||
|
%$ if t(1)
|
||
|
%$ t(2) = dyn_assert(ts.freq,4);
|
||
|
%$ t(3) = dyn_assert(ts.init.freq,4);
|
||
|
%$ t(4) = dyn_assert(ts.init.time,[1950, 1]);
|
||
|
%$ t(5) = dyn_assert(ts.vobs,1);
|
||
|
%$ t(6) = dyn_assert(ts.nobs,200);
|
||
|
%$ end
|
||
|
%$
|
||
|
%$ % Show results
|
||
|
%$ if plot_flag
|
||
|
%$ plot(x,'-k');
|
||
|
%$ hold on
|
||
|
%$ plot(ts.data,'--r');
|
||
|
%$ hold off
|
||
|
%$ axis tight
|
||
|
%$ end
|
||
|
%$
|
||
|
%$ T = all(t);
|
||
|
%@eof:1
|