computes autocovariance estimates
for a vector time series
- COVLAG( , )
The inputs to the COVLAG function are as follows:
- is an matrix of time series
values; is the number of observations, and
is the dimension of the random vector.
- is a scalar, the absolute value of which
specifies the number of lags desired.
If is positive, a mean correction is made.
If is negative, no mean correction is made.
The COVLAG function computes a sequence
of lagged crossproduct matrices.
This function is useful for computing sample
autocovariance sequences for scalar or vector time series.
The value returned by the COVLAG
function is an
block of the matrix is the sum
th row of
> 0, then the
block of the matrix is
is a row vector of the column means of
For example, the following statements produce the matrix
, as shown:
COV 1 row 4 cols (numeric)
33 23.1 13.6 4.9
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