LongMemory.jl: Generating, Estimating, and Forecasting Long Memory Models in Julia
Abstract
LongMemory.jl is a package for time series long memory modelling in Julia. The package provides functions to generate long memory, estimate model parameters, and forecast. Generating methods include fractional differencing, stochastic error duration, and cross-sectional aggregation. Estimators include the classic ones used to estimate the Hurst effect, those inspired by log-periodogram regression, and parametric ones. Forecasting is provided for all parametric estimators. Moreover, the package adds plotting capabilities to illustrate long memory dynamics and forecasting. This article presents the theoretical developments for long memory modelling, show examples using the data included with the package, and compares the properties of LongMemory.jl with current alternatives, including benchmarks. For some of the theoretical developments, LongMemory.jl provides the first publicly available implementation in any programming language. A notable feature of this package is that all functions are implemented in the same programming language, taking advantage of the ease of use and speed provided by Julia. Therefore, all code is accessible to the user. Multiple dispatch, a novel feature of the language, is used to speed computations and provide consistent calls to related methods. The package is related to the R packages longMemoryTS fracdiff.
Download
The paper can be freely downloaded here.
The package
The package is available in the Julia general registry and can be installed using the Julia REPL.
The repository is github.com/everval/LongMemory.jl and the documentation is everval.github.io/LongMemory.jl/.
More information about the package can be found here.
Recommended citation
Vera-Valdés, J.E. (2024). “LongMemory.jl: Generating, Estimating, and Forecasting Long Memory Models in Julia”. arXiv 2401.14077. https://arxiv.org/abs/2401.14077
@article{VERAVALDES2024a,
title = {LongMemory.jl: Generating, Estimating, and Forecasting Long Memory Models in Julia},
year = {2024},
author = {Vera-Valdés, J.E.},
journal = {arXiv preprint arXiv:2401.14077},
url = {https://arxiv.org/abs/2401.14077}
}