DWNARDL {DWaveNARDL} | R Documentation |
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)