CovEsts: Nonparametric Autocovariance Estimation and Analysis
Abstract
The paper introduces the R package CovEsts, which implements several nonparametric estimators for the autocovariance function. First, it presents the theoretical foundations of the implemented estimators, their properties, assumptions, and potential limitations. Next, it outlines the structure of the package and its key functions, including several methods for estimating autocovariance functions, constructing corresponding bootstrap confidence regions, and correcting the provided estimators. The package also includes diagnostic tools, such as several metrics for comparing estimators, and additional functions for broader use. The article illustrates a high degree of flexibility of the package in the selection of function parameters and the tuning of the estimators. Applications of selected estimators and package functions are illustrated using simulated data, yearly sunspot counts, and US unemployment increments data.
Versions
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BibTeX
@misc{bilchouris2026covests,
title = {{CovEsts: Nonparametric Autocovariance Estimation and Analysis}},
author = {Adam Bilchouris and Andriy Olenko},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37154},
eprint = {2609.37154},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37154},
}
RIS
TY - UNPB
TI - CovEsts: Nonparametric Autocovariance Estimation and Analysis
AU - Adam Bilchouris
AU - Andriy Olenko
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37154
UR - https://arxiv.org/abs/2609.37154
AB - The paper introduces the R package CovEsts, which implements several nonparametric estimators for the autocovariance function. First, it presents the theoretical foundations of the implemented estimators, their properties, assumptions, and potential limitations. Next, it outlines the structure of the package and its key functions, including several methods for estimating autocovariance functions, constructing corresponding bootstrap confidence regions, and correcting the provided estimators. The package also includes diagnostic tools, such as several metrics for comparing estimators, and additional functions for broader use. The article illustrates a high degree of flexibility of the package in the selection of function parameters and the tuning of the estimators. Applications of selected estimators and package functions are illustrated using simulated data, yearly sunspot counts, and US unemployment increments data.
ER -