ARest {funtimes} | R Documentation |
Estimation of Autoregressive (AR) Parameters
Description
Estimate parameters of autoregressive time series model
by default using robust difference-based estimator and Bayesian information
criterion (BIC) to select the order . This function is employed
for time series filtering in the functions
notrend_test
, sync_test
,
and wavk_test
.
Usage
ARest(x, ar.order = NULL, ar.method = "HVK", ic = c("BIC", "AIC", "none"))
Arguments
x |
a vector containing a univariate time series. Missing values are not allowed. |
ar.order |
order of the autoregressive model when |
ar.method |
method of estimating autoregression coefficients.
Default |
ic |
information criterion used to select the order of autoregressive filter (AIC of BIC),
considering models of orders |
Details
The formula for information criteria used consistently for all methods:
where =
length(x)
,
is the autoregressive order (
is the number of model parameters),
and
is the penalty (
in BIC, and
in AIC).
Value
A vector of estimated AR coefficients. Returns numeric(0)
if
the final .
Author(s)
Vyacheslav Lyubchich
References
Hall P, Van Keilegom I (2003). “Using difference-based methods for inference in nonparametric regression with time series errors.” Journal of the Royal Statistical Society, Series B (Statistical Methodology), 65(2), 443–456. doi:10.1111/1467-9868.00395.
See Also
ar
, HVK
,
notrend_test
, sync_test
, wavk_test
Examples
# Simulate a time series Y:
Y <- arima.sim(n = 200, list(order = c(2, 0, 0), ar = c(-0.7, -0.1)))
plot.ts(Y)
# Estimate the coefficients:
ARest(Y) # HVK, by default
ARest(Y, ar.method = "yw") # Yule--Walker
ARest(Y, ar.method = "burg") # Burg