1function o = baxter_king_filter(o, high_frequency, low_frequency, K) % --*-- Unitary tests --*--
2
3% Implementation of Baxter and King (1999) band pass filter for dseries objects. The code is adapted from
4% the one provided by Baxter and King. This filter isolates business cycle fluctuations with a period of length
5% ranging between high_frequency to low_frequency (quarters).
6%
7% INPUTS
8%  - o                  dseries object.
9%  - high_frequency     positive scalar, period length (default value is 6).
10%  - low_frequency      positive scalar, period length (default value is 32).
11%  - K                  positive scalar integer, truncation parameter (default value is 12).
12%
13% OUTPUTS
14%  - o                  dseries object.
15%
16% REMARKS
17% This filter use a (symmetric) moving average smoother, so that K observations at the beginning and at the end of the
18% sample are lost in the computation of the filter.
19
20% Copyright (C) 2013-2017 Dynare Team
21%
22% This file is part of Dynare.
23%
24% Dynare is free software: you can redistribute it and/or modify
25% it under the terms of the GNU General Public License as published by
26% the Free Software Foundation, either version 3 of the License, or
27% (at your option) any later version.
28%
29% Dynare is distributed in the hope that it will be useful,
30% but WITHOUT ANY WARRANTY; without even the implied warranty of
31% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
32% GNU General Public License for more details.
33%
34% You should have received a copy of the GNU General Public License
35% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
36
37if nargin<4 || isempty(K)
38    K = 12;
39    if nargin<3 || isempty(low_frequency)
40        % Set default number of periods corresponding to the lowest frequency.
41        low_frequency = 32;
42        if nargin<2 || isempty(high_frequency)
43            % Set default number of periods corresponding to the highest frequency.
44            high_frequency = 6;
45            if nargin<1
46                error('dseries::baxter_king_filter: I need at least one argument')
47            end
48        else
49            if high_frequency<2
50                error('dseries::baxter_king_filter: Second argument must be greater than 2!')
51            end
52            if high_frequency>low_frequency
53                error('dseries::baxter_king_filter: Second argument must be smaller than the third argument!')
54            end
55        end
56    end
57end
58
59o = copy(o);
60o.baxter_king_filter_(high_frequency, low_frequency, K);
61
62%@test:1
63%$ plot_flag = false;
64%$
65%$ % Create a dataset.
66%$ e = .2*randn(200,1);
67%$ u = randn(200,1);
68%$ stochastic_trend = cumsum(e);
69%$ deterministic_trend = .1*transpose(1:200);
70%$ x = zeros(200,1);
71%$ for i=2:200
72%$    x(i) = .75*x(i-1) + e(i);
73%$ end
74%$ y = x + stochastic_trend + deterministic_trend;
75%$
76%$ % Test the routine.
77%$ try
78%$     ts = dseries(y,'1950Q1');
79%$     ds = ts.baxter_king_filter();
80%$     xx = dseries(x,'1950Q1');
81%$     t(1) = 1;
82%$ catch
83%$     t(1) = 0;
84%$ end
85%$
86%$ if t(1)
87%$     t(2) = dassert(ds.freq, 4);
88%$     t(3) = dassert(ds.init.freq, 4);
89%$     t(4) = dassert(ds.init.time, [1953, 1]);
90%$     t(5) = dassert(ds.vobs, 1);
91%$     t(6) = dassert(ds.nobs, 176);
92%$     t(7) = dassert(ds.name{1}, 'Variable_1');
93%$     t(8) = dassert(ds.ops{1}, 'baxter_king_filter(Variable_1, 6, 32, 12)');
94%$     t(9) = dassert(ts.freq, 4);
95%$     t(10) = dassert(ts.name{1}, 'Variable_1');
96%$     t(11) = dassert(ts.init.freq, 4);
97%$     t(12) = dassert(ts.init.time, [1950, 1]);
98%$     t(13) = dassert(ts.vobs, 1);
99%$     t(14) = dassert(ts.nobs, length(y));
100%$     t(15) = dassert(ts.data, y);
101%$     t(16) = isempty(ts.ops{1});
102%$ end
103%$
104%$ % Show results
105%$ if plot_flag
106%$     plot(xx(ts.dates).data,'-k');
107%$     hold on
108%$     plot(ts.data,'--r');
109%$     hold off
110%$     axis tight
111%$     id = get(gca,'XTick');
112%$     set(gca,'XTickLabel',strings(ts.dates(id)));
113%$     legend({'Stationary component of y', 'Filtered y'})
114%$     print('-depsc2','../doc/dynare.plots/BaxterKingFilter.eps')
115%$     system('convert -density 300 ../doc/dynare.plots/BaxterKingFilter.eps ../doc/dynare.plots/BaxterKingFilter.png');
116%$     system('convert -density 300 ../doc/dynare.plots/BaxterKingFilter.eps ../doc/dynare.plots/BaxterKingFilter.pdf');
117%$     system('convert -density 300 ../doc/dynare.plots/BaxterKingFilter.eps ../doc/dynare.plots/BaxterKingFilter.jpg');
118%$ end
119%$
120%$ T = all(t);
121%@eof:1
122