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view scripts/signal/diffpara.m @ 11117:3cbc0d77db48 ss-3-3-53
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author | John W. Eaton <jwe@octave.org> |
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date | Tue, 19 Oct 2010 02:25:32 -0400 |
parents | 3140cb7a05a1 |
children | c776f063fefe |
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## Copyright (C) 1995, 1996, 1997, 1998, 1999, 2000, 2002, 2005, 2006, ## 2007 Friedrich Leisch ## ## This file is part of Octave. ## ## Octave 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. ## ## Octave 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 Octave; see the file COPYING. If not, see ## <http://www.gnu.org/licenses/>. ## -*- texinfo -*- ## @deftypefn {Function File} {[@var{d}, @var{dd}] =} diffpara (@var{x}, @var{a}, @var{b}) ## Return the estimator @var{d} for the differencing parameter of an ## integrated time series. ## ## The frequencies from @math{[2*pi*a/t, 2*pi*b/T]} are used for the ## estimation. If @var{b} is omitted, the interval ## @math{[2*pi/T, 2*pi*a/T]} is used. If both @var{b} and @var{a} are ## omitted then @math{a = 0.5 * sqrt (T)} and @math{b = 1.5 * sqrt (T)} ## is used, where @math{T} is the sample size. If @var{x} is a matrix, ## the differencing parameter of each column is estimated. ## ## The estimators for all frequencies in the intervals ## described above is returned in @var{dd}. The value of @var{d} is ## simply the mean of @var{dd}. ## ## Reference: P.J. Brockwell & R.A. Davis. @cite{Time Series: ## Theory and Methods}. Springer 1987. ## @end deftypefn ## Author: FL <Friedrich.Leisch@ci.tuwien.ac.at> ## Description: Estimate the fractional differencing parameter function [d, D] = diffpara (X, a, b) if ((nargin < 1) || (nargin > 3)) print_usage (); else if (isvector (X)) n = length (X); k = 1; X = reshape (X, n, 1); else [n, k] = size(X); endif if (nargin == 1) a = 0.5 * sqrt (n); b = 1.5 * sqrt (n); elseif (nargin == 2) b = a; a = 1; endif endif if (! (isscalar (a) && isscalar (b))) error ("diffpara: a and b must be scalars"); endif D = zeros (b - a + 1, k); for l = 1:k w = 2 * pi * (1 : n-1) / n; x = 2 * log (abs (1 - exp (-i*w))); y = log (periodogram (X(2:n,l))); x = center (x); y = center (y); for m = a:b D(m-a+1) = - x(1:m) * y(1:m) / sumsq (x(1:m)); endfor endfor d = mean (D); endfunction