Dual Wavelet Based NARDL Model
Overview
Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. doi:10.1016/j.intfin.2014.11.011.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from: ‘roxygen2’ All declared Imports should be used.
- NOTE11 OK, 2 NOTE2026-06-09
- ERROR10 OK, 2 NOTE, 1 ERROR2026-06-08
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2025-04-30 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-04-30
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 2.7 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("DWaveNARDL")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.