635 lines
17 KiB
Matlab
635 lines
17 KiB
Matlab
function B = subsref(A, S) % --*-- Unitary tests --*--
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%@info:
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%! @deftypefn {Function File} {@var{us} =} subsref (@var{ts},S)
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%! @anchor{@dseries/subsref}
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%! @sp 1
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%! Overloads the subsref 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 ts
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%! Dynare time series object instantiated by @ref{dseries}.
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%! @item S
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%! Matlab's structure array S with two fields, type and subs. The type field is string containing '()', '@{@}', or '.', where '()' specifies
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%! integer subscripts, '@{@}' specifies cell array subscripts, and '.' specifies subscripted structure fields. The subs field is a cell array
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%! or a string containing the actual subscripts (see matlab's documentation).
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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 us
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%! Dynare time series object. Depending on the calling sequence @var{us} is a transformation of @var{ts} obtained by applying a public method on @var{ts},
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%! or a dseries object built by extracting a variable from @var{ts}, or a dseries object containing a subsample of the all the variable in @var{ts}.
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%! @end table
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%! @sp 2
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%! @strong{Example 1.} Let @var{ts} be a dseries object containing three variables named 'A1', 'A2' and 'A3'. Then the following syntax:
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%! @example
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%! us = ts.A1;
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%! @end example
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%!will create a new dseries object @var{us} containing the variable 'A1'.
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%! @sp 1
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%! @strong{Example 2.} Let @var{ts} be a dseries object. Then the following syntax:
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%! @example
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%! us = ts.log;
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%! @end example
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%!will create a new dseries object @var{us} containing all the variables of @var{ts} transformed by the neperian logarithm.
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%! @sp 1
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%! @strong{Example 3.} Let @var{ts} be a dseries object. The following syntax:
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%! @example
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%! us = ts(3:50);
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%! @end example
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%!will create a new dseries object @var{us} by selecting a subsample out of @var{ts}.
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2011-2013 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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switch S(1).type
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case '.'
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switch S(1).subs
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case {'data','nobs','vobs','name','tex','freq','dates','init'} % Public members.
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if length(S)>1 && isequal(S(2).type,'()') && isempty(S(2).subs)
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error(['dseries::subsref: ' S(1).subs ' is not a method but a member!'])
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end
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B = builtin('subsref', A, S(1));
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case {'log','exp','ygrowth','qgrowth','ydiff','qdiff'} % Give "dot access" to public methods without args.
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B = feval(S(1).subs,A);
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if length(S)>1 && isequal(S(2).type,'()') && isempty(S(2).subs)
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S = shiftS(S);
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end
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case {'lag','lead','hptrend','hpcycle'} % Methods with less than two arguments.
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if length(S)>1 && isequal(S(2).type,'()')
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if isempty(S(2).subs)
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B = feval(S(1).subs,A);
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S = shiftS(S);
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else
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if length(S(2).subs{1})>1
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error(['dseries::subsref: ' S(1).subs{1} ' method admits no more than one argument!'])
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end
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B = feval(S(1).subs,A,S(2).subs{1});
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S = shiftS(S);
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end
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else
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B = feval(S(1).subs,A);
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end
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case {'cumsum','insert','pop'} % Methods with less than three argument.
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if length(S)>1 && isequal(S(2).type,'()')
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if isempty(S(2).subs)
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B = feval(S(1).subs,A);
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S = shiftS(S);
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else
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if length(S(2).subs)>2
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error(['dseries::subsref: ' S(1).subs{1} ' method admits no more than two arguments!'])
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end
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B = feval(S(1).subs,A,S(2).subs{:});
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S = shiftS(S);
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end
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else
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B = feval(S(1).subs,A);
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end
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case 'baxter_king_filter'
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if length(S)>1 && isequal(S(2).type,'()')
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if isempty(S(2).subs)
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B = feval(S(1).subs,A);
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S = shiftS(S);
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else
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B = feval(S(1).subs,A,S(2).subs{1})
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S = shiftS(S);
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end
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else
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B = feval(S(1).subs,A);
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end
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case 'save' % Save dseries object on disk (default is a csv file).
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B = NaN;
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if isequal(length(S),2)
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if strcmp(S(2).type,'()')
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if isempty(S(2).subs)
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save(A,inputname(1));
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else
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if isempty(S(2).subs{1})
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save(A,inputname(1),S(2).subs{2});
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else
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save(A,S(2).subs{:});
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end
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end
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S = shiftS(S);
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else
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error('dseries::subsref: Wrong syntax.')
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end
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elseif isequal(length(S),1)
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save(A,inputname(1));
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else
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error('dseries::subsref: Call to save method must come in last position!')
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end
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case 'size'
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if isequal(length(S),2) && strcmp(S(2).type,'()')
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if isempty(S(2).subs)
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[x,y] = size(A);
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B = [x, y];
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else
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B = size(A,S(2).subs{1});
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end
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S = shiftS(S);
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elseif isequal(length(S),1)
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[x,y] = size(A);
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B = [x, y];
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else
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error('dseries::subsref: Call to size method must come in last position!')
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end
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case {'set_names','rename','tex_rename'}
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B = feval(S(1).subs,A,S(2).subs{:});
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S = shiftS(S);
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otherwise % Extract a sub-object by selecting one variable.
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ndx = find(strcmp(S(1).subs,A.name));
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if ~isempty(ndx)
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B = dseries();
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B.data = A.data(:,ndx);
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B.name = A.name(ndx);
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B.tex = A.tex(ndx);
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B.tex = deblank(A.tex(ndx,:));
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B.nobs = A.nobs;
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B.vobs = 1;
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B.freq = A.freq;
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B.init = A.init;
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B.dates = A.dates;
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else
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error('dseries::subsref: Unknown public method, public member or variable!')
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end
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end
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case '()'
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if ischar(S(1).subs{1})
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% If ts is an empty dseries object, populate this object by reading data in a file.
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if isempty(A)
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B = dseries(S(1).subs{1});
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else
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error(['dseries::subsref: dseries object ''' inputname(1) ''' is not empty!'])
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end
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elseif isa(S(1).subs{1},'dynTimeIndex')
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% shift backward/forward (lag/lead) dseries object
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shift = S(1).subs{1}.index;
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if shift>0
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B = feval('lead',A,shift);
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elseif shift<0
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B = feval('lag',A,-shift);
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else
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% Do nothing.
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B = A;
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end
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elseif isscalar(S(1).subs{1}) && isnumeric(S(1).subs{1}) && isint(S(1).subs{1})
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% Input is also interpreted as a backward/forward operator
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if S(1).subs{1}>0
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B = feval('lead', A, S(1).subs{1});
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elseif S(1).subs{1}<0
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B = feval('lag', A, -S(1).subs{1});
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else
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% Do nothing.
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B = A;
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end
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elseif isdates(S(1).subs{1})
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% Extract a subsample using a dates object
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[junk,tdx] = intersect(A.dates.time,S(1).subs{1}.time,'rows');
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B = dseries();
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B.data = A.data(tdx,:);
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B.name = A.name;
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B.tex = A.tex;
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B.nobs = length(tdx);
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B.vobs = A.vobs;
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B.freq = A.freq;
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B.init = A.init+(tdx(1)-1);
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B.dates = A.dates(tdx);
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elseif isvector(S(1).subs{1}) && all(isint(S(1).subs{1}))
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% Extract a subsample using a vector of integers (observation index).
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% Note that this does not work if S(1).subs is an integer scalar... In which case S(1).subs is interpreted as a lead/lag operator (as in the Dynare syntax).
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% To extract one observation, a dates with one element input must be used.
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if all(S(1).subs{1}>0) && all(S(1).subs{1}<=A.nobs)
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if size(A.data,2)>1
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S(1).subs = [S(1).subs, ':'];
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end
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B.data = builtin('subsref', A.data, S(1));
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B.nobs = size(B.data,1);
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B.vobs = A.vobs;
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B.freq = A.freq;
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B.dates = A.dates(S(1).subs{1});
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B.init = B.dates(1);
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B.name = A.name;
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B.tex = A.tex;
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else
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error('dseries::subsref: Indices are out of bounds!')
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end
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else
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error('dseries::subsref: I have no idea of what you are trying to do!')
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end
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case '{}'
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if iscellofchar(S(1).subs)
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B = extract(A,S(1).subs{:});
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elseif isequal(length(S(1).subs),1) && all(isint(S(1).subs{1}))
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idx = S(1).subs{1};
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if max(idx)>A.vobs || min(idx)<1
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error('dseries::subsref: Indices are out of bounds!')
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end
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B = dseries();
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B.data = A.data(:,idx);
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B.name = A.name(idx);
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B.tex = A.tex(idx);
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B.nobs = A.nobs;
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B.vobs = length(idx);
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B.freq = A.freq;
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B.init = A.init;
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B.dates = A.dates;
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else
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error('dseries::subsref: What the Hell are you tryin'' to do?!')
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end
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otherwise
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error('dseries::subsref: What the Hell are you doin'' here?!')
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end
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S = shiftS(S);
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if ~isempty(S)
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B = subsref(B, S);
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end
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%@test:1
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%$ % Define a data set.
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%$ A = [transpose(1:10),2*transpose(1:10)];
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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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%$ % Instantiate a time series object.
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%$ ts1 = dseries(A,[],A_name,[]);
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%$
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%$ % Call the tested method.
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%$ a = ts1(2:9);
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%$
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%$ % Expected results.
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%$ e.data = [transpose(2:9),2*transpose(2:9)];
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%$ e.nobs = 8;
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%$ e.vobs = 2;
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%$ e.name = {'A1';'A2'};
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%$ e.freq = 1;
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%$ e.init = dates(1,2);
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%$
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%$ % Check the results.
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%$ t(1) = dyn_assert(a.data,e.data);
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%$ t(2) = dyn_assert(a.nobs,e.nobs);
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%$ t(3) = dyn_assert(a.vobs,e.vobs);
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%$ t(4) = dyn_assert(a.freq,e.freq);
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%$ t(5) = dyn_assert(isequal(a.init,e.init),1);
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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 data set.
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%$ A = [transpose(1:10),2*transpose(1:10)];
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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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%$ % Instantiate a time series object.
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%$ ts1 = dseries(A,[],A_name,[]);
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%$
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%$ % Call the tested method.
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%$ a = ts1.A1;
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%$
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%$ % Expected results.
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%$ e.data = transpose(1:10);
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%$ e.nobs = 10;
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%$ e.vobs = 1;
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%$ e.name = {'A1'};
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%$ e.freq = 1;
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%$ e.init = dates(1,1);
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%$
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%$ % Check the results.
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%$ t(1) = dyn_assert(a.data,e.data);
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%$ t(2) = dyn_assert(isequal(a.init,e.init),1);
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%$ t(3) = dyn_assert(a.nobs,e.nobs);
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%$ t(4) = dyn_assert(a.vobs,e.vobs);
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%$ t(5) = dyn_assert(a.freq,e.freq);
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%$ T = all(t);
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%@eof:2
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%@test:3
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%$ % Define a data set.
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%$ A = [transpose(1:10),2*transpose(1:10)];
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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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%$ % Instantiate a time series object.
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%$ ts1 = dseries(A,[],A_name,[]);
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%$
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%$ % Call the tested method.
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%$ a = ts1.log;
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%$
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%$ % Expected results.
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%$ e.data = log(A);
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%$ e.nobs = 10;
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%$ e.vobs = 2;
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%$ e.name = {'A1';'A2'};
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%$ e.freq = 1;
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%$ e.init = dates(1,1);
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%$
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%$ % Check the results.
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%$ t(1) = dyn_assert(a.data,e.data);
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%$ t(2) = dyn_assert(a.nobs,e.nobs);
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%$ t(3) = dyn_assert(a.vobs,e.vobs);
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%$ t(4) = dyn_assert(a.freq,e.freq);
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%$ t(5) = dyn_assert(isequal(a.init,e.init),1);
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%$ T = all(t);
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%@eof:3
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%@test:4
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%$ % Create an empty dseries object.
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%$ dataset = dseries();
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%$
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%$ t = zeros(5,1);
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%$
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%$ try
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%$ A = dataset('dynseries_test_data.csv');
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%$ t(1) = 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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%$ % Check the results.
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%$ if length(t)>1
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%$ t(2) = dyn_assert(A.nobs,4);
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%$ t(3) = dyn_assert(A.vobs,4);
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%$ t(4) = dyn_assert(A.freq,4);
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%$ t(5) = dyn_assert(isequal(A.init,dates('1990Q1')),1);
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%$ end
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%$ T = all(t);
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%@eof:4
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%@test:5
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%$ % Define a data set.
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%$ A = [transpose(1:10),2*transpose(1:10),3*transpose(1:10)];
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%$
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%$ % Define names
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%$ A_name = {'A1';'A2';'B1'};
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%$
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%$ % Instantiate a time series object.
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%$ ts1 = dseries(A,[],A_name,[]);
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%$
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%$ % Call the tested method.
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%$ a = ts1{'A1','B1'};
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%$
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%$ % Expected results.
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%$ e.data = A(:,[1,3]);
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%$ e.nobs = 10;
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%$ e.vobs = 2;
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%$ e.name = {'A1';'B1'};
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%$ e.freq = 1;
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%$ e.init = dates(1,1);
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%$
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%$ t(1) = dyn_assert(e.data,a.data);
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%$ t(2) = dyn_assert(e.nobs,a.nobs);
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%$ t(3) = dyn_assert(e.vobs,a.vobs);
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%$ t(4) = dyn_assert(e.name,a.name);
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%$ t(5) = dyn_assert(isequal(e.init,a.init),1);
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%$ T = all(t);
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%@eof:5
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%@test:6
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%$ % Define a data set.
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%$ A = rand(10,24);
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%$
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%$ % Define names
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%$ A_name = {'GDP_1';'GDP_2';'GDP_3'; 'GDP_4'; 'GDP_5'; 'GDP_6'; 'GDP_7'; 'GDP_8'; 'GDP_9'; 'GDP_10'; 'GDP_11'; 'GDP_12'; 'HICP_1';'HICP_2';'HICP_3'; 'HICP_4'; 'HICP_5'; 'HICP_6'; 'HICP_7'; 'HICP_8'; 'HICP_9'; 'HICP_10'; 'HICP_11'; 'HICP_12';};
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%$
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%$ % Instantiate a time series object.
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%$ ts1 = dseries(A,[],A_name,[]);
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%$
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%$ % Call the tested method.
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%$ a = ts1{'GDP_[0-9]'};
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%$ b = ts1{'[A-Z]_1$'};
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%$
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%$ % Expected results.
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%$ e1.data = A(:,1:12);
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%$ e1.nobs = 10;
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%$ e1.vobs = 12;
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%$ e1.name = {'GDP_1';'GDP_2';'GDP_3'; 'GDP_4'; 'GDP_5'; 'GDP_6'; 'GDP_7'; 'GDP_8'; 'GDP_9'; 'GDP_10'; 'GDP_11'; 'GDP_12'};
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%$ e1.freq = 1;
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%$ e1.init = dates(1,1);
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%$ e2.data = A(:,[1 13]);
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%$ e2.nobs = 10;
|
|
%$ e2.vobs = 2;
|
|
%$ e2.name = {'GDP_1';'HICP_1'};
|
|
%$ e2.freq = 1;
|
|
%$ e2.init = dates(1,1);
|
|
%$
|
|
%$ % Check results.
|
|
%$ t(1) = dyn_assert(e1.data,a.data);
|
|
%$ t(2) = dyn_assert(e1.nobs,a.nobs);
|
|
%$ t(3) = dyn_assert(e1.vobs,a.vobs);
|
|
%$ t(4) = dyn_assert(e1.name,a.name);
|
|
%$ t(5) = dyn_assert(isequal(e1.init,a.init),1);
|
|
%$ t(6) = dyn_assert(e2.data,b.data);
|
|
%$ t(7) = dyn_assert(e2.nobs,b.nobs);
|
|
%$ t(8) = dyn_assert(e2.vobs,b.vobs);
|
|
%$ t(9) = dyn_assert(e2.name,b.name);
|
|
%$ t(10) = dyn_assert(isequal(e2.init,b.init),1);
|
|
%$ T = all(t);
|
|
%@eof:6
|
|
|
|
%@test:7
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:10),2*transpose(1:10)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ try
|
|
%$ ts1 = dseries(A,[],A_name,[]);
|
|
%$ ts1.save('ts1');
|
|
%$ t = 1;
|
|
%$ catch
|
|
%$ t = 0;
|
|
%$ end
|
|
%$
|
|
%$ T = all(t);
|
|
%@eof:7
|
|
|
|
%@test:8
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:10),2*transpose(1:10)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ try
|
|
%$ ts1 = dseries(A,[],A_name,[]);
|
|
%$ ts1.save('test_generated_data_file','m');
|
|
%$ t = 1;
|
|
%$ catch
|
|
%$ t = 0;
|
|
%$ end
|
|
%$
|
|
%$ T = all(t);
|
|
%@eof:8
|
|
|
|
%@test:9
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:60),2*transpose(1:60),3*transpose(1:60)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2';'B1'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ ts1 = dseries(A,'1971Q1',A_name,[]);
|
|
%$
|
|
%$ % Define the range of a subsample.
|
|
%$ range = dates('1971Q2'):dates('1971Q4');
|
|
%$ % Call the tested method.
|
|
%$ a = ts1(range);
|
|
%$
|
|
%$ % Expected results.
|
|
%$ e.data = A(2:4,:);
|
|
%$ e.nobs = 3;
|
|
%$ e.vobs = 3;
|
|
%$ e.name = {'A1';'A2';'B1'};
|
|
%$ e.freq = 4;
|
|
%$ e.init = dates('1971Q2');
|
|
%$
|
|
%$ t(1) = dyn_assert(e.data,a.data);
|
|
%$ t(2) = dyn_assert(e.nobs,a.nobs);
|
|
%$ t(3) = dyn_assert(e.vobs,a.vobs);
|
|
%$ t(4) = dyn_assert(e.name,a.name);
|
|
%$ t(5) = dyn_assert(isequal(e.init,a.init),1);
|
|
%$ T = all(t);
|
|
%@eof:9
|
|
|
|
%@test:10
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:60),2*transpose(1:60),3*transpose(1:60)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2';'B1'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ ts1 = dseries(A,'1971Q1',A_name,[]);
|
|
%$
|
|
%$ % Test the size method.
|
|
%$ B = ts1.size();
|
|
%$ C = ts1.size(1);
|
|
%$ D = ts1.size(2);
|
|
%$ E = ts1.size;
|
|
%$
|
|
%$ t(1) = dyn_assert(B,[60, 3]);
|
|
%$ t(2) = dyn_assert(E,[60, 3]);
|
|
%$ t(3) = dyn_assert(C,60);
|
|
%$ t(4) = dyn_assert(D,3);
|
|
%$ T = all(t);
|
|
%@eof:10
|
|
|
|
%@test:11
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:60),2*transpose(1:60),3*transpose(1:60)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2';'B1'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ ts1 = dseries(A,'1971Q1',A_name,[]);
|
|
%$
|
|
%$ % Test the size method.
|
|
%$ B = ts1{1};
|
|
%$ C = ts1{[1,3]};
|
|
%$ D = ts1{'A1'};
|
|
%$
|
|
%$ t(1) = dyn_assert(B.name{1},'A1');
|
|
%$ t(2) = dyn_assert(B.data,A(:,1));
|
|
%$ t(3) = dyn_assert(C.name{1},'A1');
|
|
%$ t(4) = dyn_assert(C.data(:,1),A(:,1));
|
|
%$ t(5) = dyn_assert(C.name{2},'B1');
|
|
%$ t(6) = dyn_assert(C.data(:,2),A(:,3));
|
|
%$ t(7) = dyn_assert(D.name{1},'A1');
|
|
%$ t(8) = dyn_assert(D.data,A(:,1));
|
|
%$ T = all(t);
|
|
%@eof:11
|
|
|
|
%@test:12
|
|
%$ % Define a data set.
|
|
%$ A = [transpose(1:10),2*transpose(1:10)];
|
|
%$
|
|
%$ % Define names
|
|
%$ A_name = {'A1';'A2'};
|
|
%$
|
|
%$ % Instantiate a time series object.
|
|
%$ try
|
|
%$ ts1 = dseries(A,[],A_name,[]);
|
|
%$ if isoctave
|
|
%$ ts1.save('ts1');
|
|
%$ else
|
|
%$ ts1.save();
|
|
%$ end
|
|
%$ t = 1;
|
|
%$ catch
|
|
%$ t = 0;
|
|
%$ end
|
|
%$
|
|
%$ T = all(t);
|
|
%@eof:12
|
|
|
|
%@test:13
|
|
%$ try
|
|
%$ data = transpose(0:1:50);
|
|
%$ ts = dseries(data,'1950Q1');
|
|
%$ a = ts.lag;
|
|
%$ b = ts.lead;
|
|
%$ tt = dynTimeIndex();
|
|
%$ c = ts(tt-1);
|
|
%$ d = ts(tt+1);
|
|
%$ t(1) = 1;
|
|
%$ catch
|
|
%$ t(1) = 0;
|
|
%$ end
|
|
%$
|
|
%$ if t(1)>1
|
|
%$ t(2) = (a==c);
|
|
%$ t(3) = (b==d);
|
|
%$ end
|
|
%$
|
|
%$ T = all(t);
|
|
%@eof:13
|
|
|
|
%@test:14
|
|
%$ try
|
|
%$ data = transpose(0:1:50);
|
|
%$ ts = dseries(data,'1950Q1');
|
|
%$ a = ts.lag;
|
|
%$ b = ts.lead;
|
|
%$ c = ts(-1);
|
|
%$ d = ts(1);
|
|
%$ t(1) = 1;
|
|
%$ catch
|
|
%$ t(1) = 0;
|
|
%$ end
|
|
%$
|
|
%$ if t(1)>1
|
|
%$ t(2) = (a==c);
|
|
%$ t(3) = (b==d);
|
|
%$ end
|
|
%$
|
|
%$ T = all(t);
|
|
%@eof:14 |