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)

[Package DWaveNARDL version 0.1.0 Index]