Description Usage Arguments Details Value Author(s) References See Also Examples
Estimate coefficients in non-parametric autoregression using the difference-based approach by \insertCiteHall_VanKeilegom_2003;textualfuntimes.
1 |
X |
univariate time series. Missing values are not allowed. |
m1, m2 |
subsidiary smoothing parameters. Default
|
ar.order |
order of the non-parametric autoregression (specified by user). |
First, autocovariances are estimated using formula (2.6) by \insertCiteHall_VanKeilegom_2003;textualfuntimes:
\hat{γ}(0)=\frac{1}{m_2-m_1+1}∑_{m=m_1}^{m_2} \frac{1}{2(n-m)}∑_{i=m+1}^{n}\{(D_mX)_i\}^2,
\hat{γ}(j)=\hat{γ}(0)-\frac{1}{2(n-j)}∑_{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.
Vector of length ar.order
with estimated autoregression coefficients.
Yulia R. Gel, Vyacheslav Lyubchich, Xingyu Wang
1 2 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.