| Type: | Package |
| Title: | Dual Wavelet Based NARDL Model |
| Version: | 0.1.0 |
| Author: | Md Yeasin [aut, cre], Ranjit Kumar Paul [aut], Ranjit Kumar Upadhyay [aut], Anita Sarkar [aut], Amrit Kumar Paul [aut] |
| Maintainer: | Md Yeasin <yeasin.iasri@gmail.com> |
| Description: | 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>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | nardl, wavelets, stats, roxygen2 |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2025-04-29 04:47:28 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2025-04-30 08:20:02 UTC |
Wavelet-based NARDL Model
Description
This function implements the Wavelet-based Nonlinear Autoregressive Distributed Lag (WNARDL) model using wavelet transform.
Usage
DWNARDL(ts, Filter = "haar", Wvlevels = NULL, Exo, MaxLag = 3, Trend = TRUE)
Arguments
ts |
A time series object (numeric vector) for the dependent variable. |
Filter |
Wavelet filter to use (default is "haar"). |
Wvlevels |
Number of wavelet decomposition levels. Default is calculated based on the length of 'ts'. |
Exo |
A time series object (numeric vector) for the exogenous variable. |
MaxLag |
Maximum number of lags to consider. Default is 3. |
Trend |
Boolean to include trend in the model. Default is TRUE. |
Value
A list containing:
Coefficients |
Model coefficients (short and long run). |
AsymTest |
Wald test statistics and p-values. |
IC |
Information criteria (AIC, BIC, Log-likelihood). |
References
Jammazi, R., Lahiani, A., & Nguyen, D. K. (2015). A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices. *Journal of International Financial Markets, Institutions and Money, 34*, 173-187. https://doi.org/10.1016/j.intfin.2014.11.011
Examples
ts <- rnorm(100)
Exo <- rnorm(100)
Results <- DWNARDL(ts, Filter = "haar", Exo = Exo, MaxLag = 3)
Dual Wavelet-based NARDL Model
Description
This function implements the Dual Wavelet-based Nonlinear Autoregressive Distributed Lag (NARDL) model.
Usage
NARDL(Data, Exo, MaxLag, Trend = TRUE)
Arguments
Data |
A time series object (numeric vector) representing the dependent variable. |
Exo |
A time series object (numeric vector) representing the exogenous variable. |
MaxLag |
Maximum number of lags to consider. |
Trend |
Boolean to include trend in the model. Default is TRUE. |
Value
A list containing:
Coefficients |
Model coefficients (short and long run). |
AsymTest |
Wald test statistics and p-values. |
IC |
Information criteria (AIC, BIC, Log-likelihood). |
References
Jammazi, R., Lahiani, A., & Nguyen, D. K. (2015). A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices. *Journal of International Financial Markets, Institutions and Money, 34*, 173-187. https://doi.org/10.1016/j.intfin.2014.11.011
Examples
Data <- rnorm(100)
Exo <- rnorm(100)
Results <- NARDL(Data, Exo, MaxLag = 3)