Description Usage Arguments Value References Examples
This method estimates the spectral density and the autocovariances of the error process via a lag-window
(or kernel) estimator (see P.J. Brockwell and R.A. Davis (1991). Time Series: Theory and Methods. Springer Science & Business Media,
page 330). The weights are computed according to a kernel K and a bandwidth h (or a lag),
to be chosen by the user. The lag can be computed automatically by using a bootstrap technique (as in Wu and Pourahmadi (2009)), via the Rboot
function.
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epsilon |
an univariate process. |
model_selec |
the order of the method. If |
model_max |
the maximal order. |
kernel_fonc |
define the kernel to use in the method. The user can give his own kernel function. |
block_size |
size of the bootstrap blocks. |
block_n |
blocks number to use for the bootstrap. |
plot |
logical. By default, |
The method returns the tapered autocovariance vector with model_selec
autocovariance terms.
model_selec |
the number of selected autocovariance terms. |
cov_st |
the estimated autocovariances. |
E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583. https://arxiv.org/abs/1906.06583.
W.B. Wu, M. Pourahmadi (2009). Banding sample autocovariance matrices of stationary processes. Statistica Sinica, pp. 1755–1768.
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