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funtimes (version 9.1)

HVK: HVK Estimator

Description

Estimate coefficients in nonparametric autoregression using the difference-based approach by Hall_VanKeilegom_2003;textualfuntimes.

Usage

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

Value

Vector of length ar.order with estimated autoregression coefficients.

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).

Author

Yulia R. Gel, Vyacheslav Lyubchich, Xingyu Wang

Details

First, autocovariances are estimated using formula (2.6) by Hall_VanKeilegom_2003;textualfuntimes: $$\hat{\gamma}(0)=\frac{1}{m_2-m_1+1}\sum_{m=m_1}^{m_2} \frac{1}{2(n-m)}\sum_{i=m+1}^{n}\{(D_mX)_i\}^2,$$ $$\hat{\gamma}(j)=\hat{\gamma}(0)-\frac{1}{2(n-j)}\sum_{i=j+1}^n\{(D_jX)_i\}^2,$$ 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.

References

See Also

ar, ARest

Examples

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

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