1function o = onesidedhptrend_(o, lambda) % --*-- Unitary tests --*--
2
3% Extracts the trend component from a dseries object using a one sided HP filter.
4%
5% INPUTS
6% - o         [dseries]   Original time series.
7% - lambda    [double]    scalar, trend smoothness parameter.
8%
9% OUTPUTS
10% - o         [dseries]   Trend component of the original time series.
11
12% Copyright (C) 2017 Dynare Team
13%
14% This file is part of Dynare.
15%
16% Dynare is free software: you can redistribute it and/or modify
17% it under the terms of the GNU General Public License as published by
18% the Free Software Foundation, either version 3 of the License, or
19% (at your option) any later version.
20%
21% Dynare is distributed in the hope that it will be useful,
22% but WITHOUT ANY WARRANTY; without even the implied warranty of
23% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
24% GNU General Public License for more details.
25%
26% You should have received a copy of the GNU General Public License
27% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
28
29if nargin>1
30    if lambda<=0
31        error(['dseries::onesidedhptrend: Lambda must be a positive integer!'])
32    end
33    if nargin>2
34        if ~isequal(init, 'hpfilter')
35            error('dseries::onesidedhptrend: Unknown option!')
36        end
37    end
38else
39    lambda = [];
40end
41
42for i=1:vobs(o)
43    if isempty(o.ops{i})
44        if isempty(lambda)
45            if nargin>2
46                o.ops(i) = {sprintf('onesidedhptrend(%s, [], ''%s'')', o.name{i}, init)};
47            else
48                o.ops(i) = {sprintf('onesidedhptrend(%s, [])', o.name{i})};
49            end
50        else
51            if nargin>2
52                o.ops(i) = {sprintf('onesidedhptrend(%s, %s, ''%s'')', o.name{i}, num2str(lambda), init)};
53            else
54                o.ops(i) = {sprintf('onesidedhptrend(%s, %s)', o.name{i}, num2str(lambda))};
55            end
56        end
57    else
58        if isempty(lambda)
59            if nargin>2
60                o.ops(i) = {sprintf('onesidedhptrend(%s, [], ''%s'')', o.ops{i}, init)};
61            else
62                o.ops(i) = {sprintf('onesidedhptrend(%s, [])', o.ops{i})};
63            end
64        else
65            if nargin>2
66                o.ops(i) = {sprintf('onesidedhptrend(%s, %s, ''%s'')', o.ops{i}, num2str(lambda), init)};
67            else
68                o.ops(i) = {sprintf('onesidedhptrend(%s, %s)', o.ops{i}, num2str(lambda))};
69            end
70        end
71    end
72end
73
74if nargin>2
75    trend = o.hptrend(lambda);
76    x0 = trend.data(1:2,:);
77    o.data = one_sided_hp_filter(o.data, lambda, x0);
78else
79    o.data = one_sided_hp_filter(o.data, lambda);
80end
81
82return
83
84%@test:1
85e = .2*randn(200,1);
86u = randn(200,1);
87stochastic_trend = cumsum(e);
88deterministic_trend = .1*transpose(1:200);
89x = zeros(200,1);
90for i=2:200
91    x(i) = .75*x(i-1) + e(i);
92end
93y = x + stochastic_trend + deterministic_trend;
94
95try
96    ts0 = dseries(y,'1950Q1');
97    ts0.onesidedhptrend_(1600);
98    t(1) = 1;
99catch
100    t(1) = 0;
101end
102
103T = all(t);
104%@eof:1
105
106%@test:2
107e = .2*randn(200,2);
108u = randn(200,2);
109stochastic_trends = cumsum(e);
110deterministic_trends = transpose(1:200)*[.1, -.2];
111x = zeros(200,2);
112for i=2:200
113    x(i, 1) = .75*x(i-1, 1) + e(i, 1);
114    x(i, 2) = -.10*x(i-1, 2) + e(i, 2);
115end
116y = x + stochastic_trends + deterministic_trends;
117
118try
119    ts0 = dseries(y,'1950Q1');
120    ts0.onesidedhptrend_(1600);
121    t(1) = 1;
122catch
123    t(1) = 0;
124end
125
126T = all(t);
127%@eof:2