peracf {perARMA} | R Documentation |
Periodic ACF function
Description
Function peracf
, given an input time series and a specified period T
, computes the periodic correlation coefficients for which
\rho(t+\tau,t)=\rho(t,\tau)
, where t = 1,\ldots, T
are seasons and \tau
is lag. For each
possible pair of t
and \tau
confidence limits for
\rho(t,\tau)
are also computed using Fisher
transformation. Procedure peracf
provides also two important tests: \rho(t+\tau,t) \equiv \rho(\tau)
and \rho(t+\tau,t) \equiv 0
.
Usage
peracf(x, T_t, tau, missval, datastr,...)
Arguments
x |
input time series, at the begining missing values
in |
T_t |
period of PC-T structure. |
tau |
vector of lag values for which estimation is made. |
missval |
notation for missing values (denoted as NaN). |
datastr |
string name of data for printing. |
... |
other arguments, that are connected with the plots: |
Details
Function peracf
uses three separate procedures:
rhoci()
returns the upper and lower bands defining a 1 - \alpha
confidence interval for the true values of
\rho(t, \tau)
,
rho.zero.test()
tests whether the estimated correlation coefficients are equal to zeros, \rho(t+\tau,t) \equiv 0
.
rho.equal.test()
tests whether the estimated correlation coefficients are equal to each other for all seasons in the period,
\rho(t+\tau,t) \equiv \rho(\tau)
.
In the test \rho(t+\tau,t) \equiv \rho(\tau)
, rejection for some \tau
> 0
indicates
that series is properly PC and is not just an amplitude modulated stationary
sequence. In other words, there exists a nonzero
lag \tau
for which \rho(t+\tau,t)
is
properly periodic in t
.
In the test \rho(t+\tau,t) \equiv 0
, the
rejection for some \tau \neq 0
indicates the sequence is not PC white noise.
Value
tables of values for each specified lag \tau
:
rho(t , tau) |
estimated correlation coefficients. |
lower |
lower bands of confidence intervals. |
upper |
upper bands of confidence intervals. |
nsamp |
number of samples used in each estimation. |
Above values are also returned as matrices.
Author(s)
Harry Hurd
References
Hurd, H. L., Miamee, A. G., (2007), Periodically Correlated Random Sequences: Spectral Theory and Practice, Wiley InterScience.
See Also
Examples
data(volumes)
dev.set(which=1)
peracf(t(volumes),24,seq(1,12),NaN,'volumes')