tvar: Time Varying Coefficients AR Model

In TSSS: Time Series Analysis with State Space Model

Time Varying Coefficients AR Model

Description

Estimate time varying coefficients AR model.

Usage

tvar(y, trend.order = 2, ar.order = 2, span, outlier = NULL, tau2.ini = NULL,
     delta, plot = TRUE)

Arguments

y	a univariate time series.
trend.order	trend order (1 or 2).
ar.order	AR order.
span	local stationary span.
outlier	positions of outliers.
tau2.ini	initial estimate of variance of the system noise \tau^2. If tau2.ini = NULL, the most suitable value is chosen in \tau^2 = 2^{-k}.
delta	search width.
plot	logical. If TRUE (default), PARCOR is plotted.

Details

The time-varying coefficients AR model is given by

y_t = a_{1,t}y_{t-1} + \ldots + a_{p,t}y_{t-p} + u_t

where a_{i,t} is i-lag AR coefficient at time t and u_t is a zero mean white noise.

The time-varying spectrum can be plotted using AR coefficient arcoef and variance of the observational noise sigma2 by tvspc.

Value

arcoef	time varying AR coefficients.
sigma2	variance of the observational noise \sigma^2.
tau2	variance of the system noise \tau^2.
llkhood	log-likelihood of the model.
aic	AIC.
parcor	PARCOR.

References

Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.

Kitagawa, G. and Gersch, W. (1996) Smoothness Priors Analysis of Time Series. Lecture Notes in Statistics, No.116, Springer-Verlag.

Kitagawa, G. and Gersch, W. (1985) A smoothness priors time varying AR coefficient modeling of nonstationary time series. IEEE trans. on Automatic Control, AC-30, 48-56.

See Also

tvspc, plot.tvspc

Examples

# seismic data
data(MYE1F)
z <- tvar(MYE1F, trend.order = 2, ar.order = 8, span = 20,
          outlier = c(630, 1026), tau2.ini = 6.6e-06, delta = 1.0e-06)
z

spec <- tvspc(z$arcoef, z$sigma2)
plot(spec)

TSSS documentation built on Sept. 29, 2023, 9:07 a.m.