NARDL

This function implements the Dual Wavelet-based Nonlinear Autoregressive Distributed Lag (NARDL) model.

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>.

Md Yeasin

DWaveNARDL

Dual Wavelet Based NARDL Model

Ranjit Kumar Paul

Ranjit Kumar Upadhyay

Anita Sarkar

Amrit Kumar Paul

NARDL function

<dl><dt>Data</dt>
<dd>A time series object (numeric vector) representing the dependent variable.</dd>
<dt>Exo</dt>
<dd>A time series object (numeric vector) representing the exogenous variable.</dd>
<dt>MaxLag</dt>
<dd>Maximum number of lags to consider.</dd>
<dt>Trend</dt>
<dd>Boolean to include trend in the model. Default is TRUE.</dd></dl>

Arguments

Dual Wavelet-based NARDL Model — NARDL

<dl>

<dt>Data</dt>
<dd>A time series object (numeric vector) representing the dependent variable.</dd>


<dt>Exo</dt>
<dd>A time series object (numeric vector) representing the exogenous variable.</dd>


<dt>MaxLag</dt>
<dd>Maximum number of lags to consider.</dd>


<dt>Trend</dt>
<dd>Boolean to include trend in the model. Default is TRUE.</dd>

</dl>

NARDL: Dual Wavelet-based NARDL Model

Description

Usage

Value

Arguments

References

Examples