diff --git a/doc/dynare.texi b/doc/dynare.texi index 5c9c60fff..6d8556303 100644 --- a/doc/dynare.texi +++ b/doc/dynare.texi @@ -9789,6 +9789,36 @@ ans is a dseries object: @sp 1 +@deftypefn{dseries} {@var{C} =} mpower (@var{A}, @var{B}) + +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)}. + +@examplehead +@example +>> ts0 = dseries(transpose(1:3)); +>> ts1 = ts0^2 + +ts1 is a dseries object: + + | power(Variable_1,2) +1Y | 1 +2Y | 4 +3Y | 9 + +>> ts2 = ts0^ts0 + +ts2 is a dseries object: + + | power(Variable_1,Variable_1) +1Y | 1 +2Y | 4 +3Y | 27 +@end example + +@end deftypefn + +@sp 1 + @deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B}) 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}. diff --git a/matlab/@dseries/mpower.m b/matlab/@dseries/mpower.m new file mode 100644 index 000000000..89b0b1049 --- /dev/null +++ b/matlab/@dseries/mpower.m @@ -0,0 +1,136 @@ +function A = mpower(B,C) % --*-- Unitary tests --*-- + +%@info: +%! @deftypefn {Function File} {@var{A} =} mpower (@var{B},@var{C}) +%! @anchor{@dseries/mpower} +%! @sp 1 +%! Overloads the mpower method for the Dynare time series class (@ref{dseries}). +%! @sp 2 +%! @strong{Inputs} +%! @sp 1 +%! @table @ @var +%! @item B +%! Dynare time series object instantiated by @ref{dseries}, with T observations and N variables. +%! @item C +%! Real scalar or a dseries object with T observations and N variables. +%! @end table +%! @sp 1 +%! @strong{Outputs} +%! @sp 1 +%! @table @ @var +%! @item A +%! dseries object with T observations and N variables. +%! @end deftypefn +%@eod: + +% 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 . + +if isdseries(B) && isnumeric(C) && isreal(C) && isscalar(C) + A = dseries(); + A.freq = B.freq; + A.init = B.init; + A.dates = B.dates; + A.nobs = B.nobs; + A.vobs = B.vobs; + A.name = cell(A.vobs,1); + A.tex = cell(A.vobs,1); + for i=1:A.vobs + A.name(i) = {['power(' B.name{i} ',' num2str(C) ')']}; + A.tex(i) = {[B.tex{i} '^' num2str(C) ]}; + end + A.data = B.data.^C; + return +end + +if isdseries(B) && isdseries(C) + if isequal(B.nobs,C.nobs) && isequal(B.vobs,C.vobs) && isequal(B.freq,C.freq) + A = dseries(); + A.freq = B.freq; + A.init = B.init; + A.dates = B.dates; + A.nobs = B.nobs; + A.vobs = B.vobs; + A.name = cell(A.vobs,1); + A.tex = cell(A.vobs,1); + for i=1:A.vobs + A.name(i) = {['power(' B.name{i} ',' C.name{i} ')']}; + A.tex(i) = {[B.tex{i} '^{' C.tex{i} '}']}; + end + A.data = B.data.^C.data; + else + error('dseries::mpower: If both input arguments are dseries objects, they must have the same numbers of variables and observations and common frequency!') + end + return +end + +error(['dseries::mpower: Wrong calling sequence!']) + +%@test:1 +%$ % Define a datasets. +%$ A = rand(10,2); B = randn(10,2); +%$ +%$ % Define names +%$ A_name = {'A1';'A2'}; B_name = {'B1';'B2'}; +%$ +%$ +%$ % Instantiate a time series object. +%$ try +%$ ts1 = dseries(A,[],A_name,[]); +%$ ts2 = dseries(B,[],B_name,[]); +%$ ts3 = ts1^ts2; +%$ t = 1; +%$ catch +%$ t = 0; +%$ end +%$ +%$ if t(1) +%$ t(2) = dyn_assert(ts3.vobs,2); +%$ t(3) = dyn_assert(ts3.nobs,10); +%$ t(4) = dyn_assert(ts3.data,A.^B,1e-15); +%$ t(5) = dyn_assert(ts3.name,{'power(A1,B1)';'power(A2,B2)'}); +%$ t(6) = dyn_assert(ts3.tex,{'A1^{B1}';'A2^{B2}'}); +%$ end +%$ T = all(t); +%@eof:1 + +%@test:2 +%$ % Define a datasets. +%$ A = rand(10,2); +%$ +%$ % Define names +%$ A_name = {'A1';'A2'}; +%$ +%$ +%$ % Instantiate a time series object. +%$ try +%$ ts1 = dseries(A,[],A_name,[]); +%$ ts3 = ts1^2; +%$ t = 1; +%$ catch +%$ t = 0; +%$ end +%$ +%$ if t(1) +%$ t(2) = dyn_assert(ts3.vobs,2); +%$ t(3) = dyn_assert(ts3.nobs,10); +%$ t(4) = dyn_assert(ts3.data,A.^2,1e-15); +%$ t(5) = dyn_assert(ts3.name,{'power(A1,2)';'power(A2,2)'}); +%$ t(6) = dyn_assert(ts3.tex,{'A1^2';'A2^2'}); +%$ end +%$ T = all(t); +%@eof:2 \ No newline at end of file