NARDL: Dual Wavelet-based NARDL Model

In DWaveNARDL: Dual Wavelet Based NARDL Model

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)

DWaveNARDL documentation built on June 8, 2025, 11:45 a.m.