
This program fits an autoregressive model by a Bayesian procedure. The least squares estimates of the parameters are obtained by the householder transformation.
unibar(y, ar.order = NULL, plot = TRUE)
mean.
variance.
innovation variance.
AIC.
minimum AIC.
AIC-aicmin
.
order of minimum AIC.
innovation variance attained at m=order.maice
.
partial autocorrelation coefficients (least squares estimate).
Bayesian Weight.
integrated Bayesian weights.
innovation variance of Bayesian model.
AIC of Bayesian model.
equivalent number of parameters.
partial autocorrelation coefficients of Bayesian model.
AR coefficients of Bayesian model.
power spectrum.
a univariate time series.
order of the AR model. Default is
y
.
logical. If TRUE
(default), daic
, pacoef
and
pspec
are plotted.
The AR model is given by
Bayesian weight of the
H.Akaike (1978) A Bayesian Extension of The Minimum AIC Procedure of Autoregressive model Fitting. Research memo. No.126. The Institute of Statistical Mathematics.
G.Kitagawa and H.Akaike (1978) A Procedure for The Modeling of Non-Stationary Time Series. Ann. Inst. Statist. Math., 30, B, 351--363.
H.Akaike, G.Kitagawa, E.Arahata and F.Tada (1979) Computer Science Monograph, No.11, Timsac78. The Institute of Statistical Mathematics.
data(Canadianlynx)
z <- unibar(Canadianlynx, ar.order = 20)
z$arcoef
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