perars {timsac} | R Documentation |
Periodic Autoregression for a Scalar Time Series
Description
This is the program for the fitting of periodic autoregressive models by the method of least squares realized through householder transformation.
Usage
perars(y, ni, lag = NULL, ksw = 0)
Arguments
y |
a univariate time series. |
ni |
number of instants in one period. |
lag |
maximum lag of periods. Default is
|
ksw |
integer. ' |
Details
Periodic autoregressive model
(
ni
) is defined
by
,
,
where is the number of periods,
is the number of instants in
one period and
is the Gaussian white noise. When
ksw
is
set to '', the constant term
is excluded.
The statistics AIC is defined by
, where
is the
length of data,
is the estimate of the innovation variance matrix and
is the number of parameters. The outputs are the estimates of the
regression coefficients and innovation variance of the periodic AR model for
each instant.
Value
mean |
mean. |
var |
variance. |
subset |
specification of i-th regressor ( |
regcoef |
regression coefficients. |
rvar |
residual variances. |
np |
number of parameters. |
aic |
AIC. |
v |
innovation variance matrix. |
arcoef |
AR coefficient matrices. |
const |
constant vector. |
morder |
order of the MAICE model. |
References
M.Pagano (1978) On Periodic and Multiple Autoregressions. Ann. Statist., 6, 1310–1317.
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
Examples
data(Airpollution)
perars(Airpollution, ni = 6, lag = 2, ksw = 1)