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HVK: HVK estimator

Description

Estimates coefficients in non-parametric autoregression using the difference-based approach by Hall and Van Keilegom (2003).

Usage

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 non-parametric autoregression (specified by user).

Value

  • Vector of length ar.order with estimated autoregression coefficients.

Details

First, autocovariances are estimated (formula (2.6) by Hall and Van Keilegom, 2003): $$\hat{\gamma}(0)=\frac{1}{m_2-m_1+1}\sum_{m=m_1}^{m_2}\frac{1}{2(n-m)}\sum_{i=m+1}^{n}{(D_mX)_i}^2,$$ $$\hat{\gamma}(j)=\hat{\gamma}(0)-\frac{1}{2(n-j)}\sum_{i=j+1}^n{(D_jX)_i}^2,$$ 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

Hall, P. and Van Keilegom, I. (2003) Using difference-based methods for inference in nonparametric regression with time series errors. J. R. Statist. Soc. B 65, Part 2, 443--456.

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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