Linear Regression with Stationary Errors: the R Package slm

This paper introduces the R package slm, which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear regression in the general context where the error process is strictly stationary with a short memory. We work in the setting of Hannan (1973), who proved the asymptotic normality of the (normalized) least squares estimators (LSE) under very mild conditions on the error process. We propose different ways to estimate the asymptotic covariance matrix of the LSE and then to correct the type I error rates of the usual tests on the parameters (as well as confidence intervals). The procedures are evaluated through different sets of simulations.

Emmanuel Caron , Jérôme Dedecker , Bertrand Michel
2020-05-01

CRAN packages used

slm, sandwich, stats, capushe

CRAN Task Views implied by cited packages

Econometrics, Finance, Robust, SocialSciences

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Citation

For attribution, please cite this work as

Caron, et al., "The R Journal: Linear Regression with Stationary Errors: the R Package slm", {The R Journal}, 2020

BibTeX citation

@article{RJ-2021-030,
  author = {Caron, Emmanuel and Dedecker, Jérôme and Michel, Bertrand},
  title = {The R Journal: Linear Regression with Stationary Errors: the R Package slm},
  journal = {{The R Journal}},
  year = {2020},
  note = {https://doi.org/10.32614/RJ-2021-030},
  doi = {10.32614/RJ-2021-030},
  volume = {13},
  issue = {1},
  issn = {2073-4859},
  pages = {83-100}
}