Added @dseries/mpower method (element-by-element powers).
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@ -9789,6 +9789,36 @@ ans is a dseries object:
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@sp 1
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@sp 1
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@deftypefn{dseries} {@var{C} =} mpower (@var{A}, @var{B})
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Overloads the @code{mpower} (@code{^}) operator for @dseries objects and computes element-by-element power. @var{A} is a @dseries object with @code{N} variables and @code{T} observations. If @var{B} is a real scalar, then @code{mpower(@var{A},@var{B})} returns a @dseries object @var{C} with @code{C.data(t,n)=A.data(t,n)^C}. If @var{B} is a @dseries object with @code{N} variables and @code{T} observations then @code{mpower(@var{A},@var{B})} returns a @dseries object @var{C} with @code{C.data(t,n)=A.data(t,n)^C.data(t,n)}.
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@examplehead
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@example
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>> ts0 = dseries(transpose(1:3));
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>> ts1 = ts0^2
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ts1 is a dseries object:
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| power(Variable_1,2)
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1Y | 1
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2Y | 4
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3Y | 9
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>> ts2 = ts0^ts0
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ts2 is a dseries object:
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| power(Variable_1,Variable_1)
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1Y | 1
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2Y | 4
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3Y | 27
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@end example
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@end deftypefn
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@sp 1
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@deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B})
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@deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B})
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Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}.
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Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}.
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@ -0,0 +1,136 @@
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function A = mpower(B,C) % --*-- Unitary tests --*--
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%@info:
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%! @deftypefn {Function File} {@var{A} =} mpower (@var{B},@var{C})
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%! @anchor{@dseries/mpower}
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%! @sp 1
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%! Overloads the mpower method for the Dynare time series class (@ref{dseries}).
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item B
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%! Dynare time series object instantiated by @ref{dseries}, with T observations and N variables.
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%! @item C
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%! Real scalar or a dseries object with T observations and N variables.
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%! @end table
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%! @sp 1
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item A
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%! dseries object with T observations and N variables.
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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if isdseries(B) && isnumeric(C) && isreal(C) && isscalar(C)
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A = dseries();
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A.freq = B.freq;
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A.init = B.init;
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A.dates = B.dates;
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A.nobs = B.nobs;
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A.vobs = B.vobs;
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A.name = cell(A.vobs,1);
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A.tex = cell(A.vobs,1);
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for i=1:A.vobs
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A.name(i) = {['power(' B.name{i} ',' num2str(C) ')']};
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A.tex(i) = {[B.tex{i} '^' num2str(C) ]};
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end
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A.data = B.data.^C;
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return
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end
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if isdseries(B) && isdseries(C)
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if isequal(B.nobs,C.nobs) && isequal(B.vobs,C.vobs) && isequal(B.freq,C.freq)
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A = dseries();
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A.freq = B.freq;
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A.init = B.init;
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A.dates = B.dates;
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A.nobs = B.nobs;
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A.vobs = B.vobs;
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A.name = cell(A.vobs,1);
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A.tex = cell(A.vobs,1);
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for i=1:A.vobs
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A.name(i) = {['power(' B.name{i} ',' C.name{i} ')']};
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A.tex(i) = {[B.tex{i} '^{' C.tex{i} '}']};
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end
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A.data = B.data.^C.data;
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else
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error('dseries::mpower: If both input arguments are dseries objects, they must have the same numbers of variables and observations and common frequency!')
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end
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return
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end
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error(['dseries::mpower: Wrong calling sequence!'])
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%@test:1
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%$ % Define a datasets.
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%$ A = rand(10,2); B = randn(10,2);
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%$
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%$ % Define names
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%$ A_name = {'A1';'A2'}; B_name = {'B1';'B2'};
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%$
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%$
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%$ % Instantiate a time series object.
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%$ try
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%$ ts1 = dseries(A,[],A_name,[]);
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%$ ts2 = dseries(B,[],B_name,[]);
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%$ ts3 = ts1^ts2;
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%$ t = 1;
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%$ catch
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%$ t = 0;
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%$ end
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%$
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%$ if t(1)
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%$ t(2) = dyn_assert(ts3.vobs,2);
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%$ t(3) = dyn_assert(ts3.nobs,10);
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%$ t(4) = dyn_assert(ts3.data,A.^B,1e-15);
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%$ t(5) = dyn_assert(ts3.name,{'power(A1,B1)';'power(A2,B2)'});
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%$ t(6) = dyn_assert(ts3.tex,{'A1^{B1}';'A2^{B2}'});
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%$ end
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%$ T = all(t);
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%@eof:1
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%@test:2
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%$ % Define a datasets.
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%$ A = rand(10,2);
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%$
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%$ % Define names
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%$ A_name = {'A1';'A2'};
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%$
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%$
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%$ % Instantiate a time series object.
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%$ try
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%$ ts1 = dseries(A,[],A_name,[]);
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%$ ts3 = ts1^2;
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%$ t = 1;
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%$ catch
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%$ t = 0;
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%$ end
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%$
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%$ if t(1)
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%$ t(2) = dyn_assert(ts3.vobs,2);
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%$ t(3) = dyn_assert(ts3.nobs,10);
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%$ t(4) = dyn_assert(ts3.data,A.^2,1e-15);
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%$ t(5) = dyn_assert(ts3.name,{'power(A1,2)';'power(A2,2)'});
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%$ t(6) = dyn_assert(ts3.tex,{'A1^2';'A2^2'});
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%$ end
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%$ T = all(t);
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%@eof:2
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