from
autocov.m
by Phillip M. Feldman
compute sample autocovariance of a time series (vector)
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| autocov(x,maxlag) |
function acv= autocov(x,maxlag)
%
% autocov computes the sample autocovariance of a time series x for lags
% from 0 to maxlag, returning a column vector of length maxlag+1. x must
% be a column vector having length m not less than maxlag+1. If no value
% is supplied for maxlag, the default is the minimum of m-1 and 100.
%
% Dr. Phillip M. Feldman
% Last update 21 June 2008
%
% Based on equations on p. 19 of "Introduction to Time Series and
% Forecasting" 2nd Edition by Brockwell and Davis.
% Section 1: Check input arguments and supply default value for maxlag
% if needed.
if nargin < 1, error('Missing input vector.'); end
[m n]= size(x);
if (n ~= 1)
error('x must be a column vector.')
end
if (m <= maxlag)
error('The length of the input vector x must be at least maxlag+1.');
end
if nargin < 2, maxlag= min(m-1,100); end
% Section 2: Compute autocovariance.
% Remove mean from x:
x= x - mean(x);
% For faster running time, we pre-allocate the output array:
acv= zeros(maxlag+1,1);
% Compute autocovariance:
for h= 0 : maxlag
% Take matrix product of row vector and column vector to obtain a
% scalar. The row vector contains the first n-h elements of x; the
% column vector contains the last n-h elements of x.
acv(h+1)= x(1:m-h)' * x(1+h:m);
end
acv= acv / m;
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