HVK function

HVK Estimator

Estimate coefficients in nonparametric autoregression using the difference-based approach by if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="Hall_VanKeilegom_2003;textual",package="funtimes",cached_env=.Rdpack.currefs) .

HVK(X, m1 = NULL, m2 = NULL, ar.order = 1)

Arguments

• X: univariate time series. Missing values are not allowed.
• m1, m2: subsidiary smoothing parameters. Default m1 = round(length(X)^(0.1)), m2 = round(length(X)^(0.5)).
• ar.order: order of the nonparametric autoregression (specified by user).

Returns

Vector of length ar.order with estimated autoregression coefficients.

Details

First, autocovariances are estimated using formula (2.6) by if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_citeOnly(keys="Hall_VanKeilegom_2003;textual",package="funtimes",cached_env=.Rdpack.currefs) :

where $n$ = length(X) is sample size, $D_j$ is a difference operator such that $(D_jX)_i=X_i-X_{i-j}$. Then, Yule--Walker method is used to derive autoregression coefficients.

Examples

X <- arima.sim(n = 300, list(order = c(1, 0, 0), ar = c(0.6)))
HVK(as.vector(X), ar.order = 1)

References

if(!exists(".Rdpack.currefs")) .Rdpack.currefs <-new.env();Rdpack::insert_all_ref(.Rdpack.currefs)

See Also

ar, ARest

Author(s)

Yulia R. Gel, Vyacheslav Lyubchich, Xingyu Wang