function s = std(o, geometric) % --*-- Unitary tests --*-- % Returns the standard deviation of the variables in a @dseries object o. % See https://en.wikipedia.org/wiki/Geometric_standard_deviation % % INPUTS % - o [dseries] T observations and N variables. % - geometric [logical] if true returns the geometric standard deviation (default is false). % % OUTPUTS % - s [double] 1*N vector. % Copyright (C) 2016-2017 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 . if nargin<2 geometric = false; end if geometric m = mean(o, true); s = exp(sqrt(sum(log(bsxfun(@rdivide, o.data, m)).^2, 1)/nobs(o))); else s = std(o.data); end %@test:1 %\$ % Define a dataset. %\$ A = repmat([1.005, 1.05], 10, 1); %\$ %\$ % Instantiate a time series object and compute the mean. %\$ try %\$ ts = dseries(A); %\$ s1 = std(ts, true); %\$ s2 = std(ts); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(s1),[1, 2]), true); %\$ t(3) = dassert(isequal(size(s2),[1, 2]), true); %\$ t(4) = dassert(s1, [1, 1]); %\$ t(4) = all(abs(s2)<1e-12); %\$ end %\$ T = all(t); %@eof:1 %@test:2 %\$ % Define a dataset. %\$ A = repmat([1.005, 1.05], 10, 1); %\$ %\$ % Instantiate a time series object and compute the mean. %\$ try %\$ ts = dseries(A); %\$ s1 = ts.std(true); %\$ s2 = ts.std(); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(s1),[1, 2]), true); %\$ t(3) = dassert(isequal(size(s2),[1, 2]), true); %\$ t(4) = dassert(s1, [1, 1]); %\$ t(4) = all(abs(s2)<1e-12); %\$ end %\$ T = all(t); %@eof:2 %@test:3 %\$ % Define a dataset. %\$ A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]); %\$ %\$ % Instantiate time series objects and compute the mean. %\$ try %\$ ts = dseries(A); %\$ s = std(ts); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(s),[1, 2]), true); %\$ t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true); %\$ end %\$ T = all(t); %@eof:3 %@test:4 %\$ % Define a dataset. %\$ A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]); %\$ %\$ % Instantiate time series objects and compute the mean. %\$ try %\$ ts = dseries(A); %\$ s = ts.std(); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(s),[1, 2]), true); %\$ t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true); %\$ end %\$ T = all(t); %@eof:4