Type: Package
Title: Fourier ARDL Methods: Quantile, Nonlinear, Multi-Threshold & Unit Root Tests
Version: 1.0.2
Date: 2026-02-25
Author: Muhammad Alkhalaf ORCID iD [aut, cre, cph], Merwan Roudane [ctb] (Original Stata/Python implementation)
Maintainer: Muhammad Alkhalaf <muhammedalkhalaf@gmail.com>
Description: Comprehensive implementation of advanced ARDL methodologies for cointegration analysis with structural breaks and asymmetric effects. Includes: (1) Fourier Quantile ARDL (FQARDL) - quantile regression with Fourier approximation for analyzing relationships across the conditional distribution; (2) Fourier Nonlinear ARDL (FNARDL) - asymmetric cointegration with partial sum decomposition following Shin, Yu & Greenwood-Nimmo (2014) <doi:10.1007/978-1-4899-8008-3_9>; (3) Multi-Threshold NARDL (MTNARDL) - multiple regime asymmetry analysis; (4) Fourier Unit Root Tests - ADF and KPSS tests with Fourier terms following Enders & Lee (2012) <doi:10.1016/j.econlet.2012.05.019> and Becker, Enders & Lee (2006) <doi:10.1111/j.1467-9892.2006.00490.x>. Features automatic lag and frequency selection, PSS bounds testing following Pesaran, Shin & Smith (2001) <doi:10.1002/jae.616>, bootstrap cointegration tests, Wald tests for asymmetry, dynamic multiplier computation, and publication-ready visualizations. Ported from Stata/Python by Dr. Merwan Roudane.
License: GPL-3
URL: https://github.com/muhammedalkhalaf/fqardl
BugReports: https://github.com/muhammedalkhalaf/fqardl/issues
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.5.0)
Imports: quantreg (≥ 5.0), ggplot2 (≥ 3.0.0), tidyr, gridExtra, stats
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, plotly, covr
RoxygenNote: 7.3.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-03-12 17:56:21 UTC; acad_
Repository: CRAN
Date/Publication: 2026-03-17 19:10:14 UTC

Fourier ARDL Methods for R

Description

Comprehensive implementation of advanced ARDL methodologies for cointegration analysis with structural breaks and asymmetric effects.

Main functions:

Author(s)

Muhammad Alkhalaf contact@rufyqelngeh.com

Contributor: Merwan Roudane (Original Stata/Python implementation)

References

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework.

Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships.

Bootstrap Bounds Test

Description

Performs bootstrap-based bounds test for cointegration, following McNown et al. (2018) methodology.

Usage

bootstrap_bounds_test(
  y,
  X,
  fourier,
  p,
  q,
  tau,
  case,
  n_boot = 1000,
  verbose = FALSE
)

Arguments

y

Dependent variable

X

Independent variables

fourier

Fourier terms

p

Lag for y

q

Lag for X

tau

Quantiles

case

Model case

n_boot

Number of bootstrap replications

verbose

Logical. Print progress messages (default: FALSE)

Value

List with bootstrap p-values

Bootstrap NARDL Test

Description

Bootstrap NARDL Test

Usage

bootstrap_nardl(y, X, fourier, p, q, case, n_boot)

Build ARDL Design Matrix

Description

Constructs the design matrix for ARDL estimation including lagged dependent and independent variables.

Usage

build_ardl_design(y, X, fourier, p, q)

Arguments

y

Dependent variable

X

Independent variables matrix

fourier

Fourier terms

p

Lag order for y

q

Lag order for X

Value

List with y and X design matrices

Build MTNARDL Regressor Matrix

Description

Build MTNARDL Regressor Matrix

Usage

build_mtnardl_regressors(data, x_names, decompose, decomposed, regime_names)

Build NARDL Regressor Matrix

Description

Build NARDL Regressor Matrix

Usage

build_nardl_regressors(data, x_names, decompose, decomposed_data)

Arguments

data

Original data

x_names

All independent variable names

decompose

Variables that are decomposed

decomposed_data

Output from decompose_variables

Value

Matrix of regressors

Compute Asymmetric Multipliers

Description

Compute Asymmetric Multipliers

Usage

compute_asymmetric_multipliers(nardl_result, decompose, x_names)

Arguments

nardl_result

NARDL estimation results

decompose

Decomposed variable names

x_names

Original variable names

Value

List with long-run and short-run asymmetric multipliers

Compute Model Diagnostics

Description

Computes various diagnostic statistics for the QARDL models.

Usage

compute_diagnostics(qardl_results)

Arguments

qardl_results

List of QARDL estimation results

Value

List of diagnostics for each quantile

Compute Long-run and Short-run Multipliers

Description

Calculates the long-run and short-run multipliers from QARDL estimates.

Usage

compute_multipliers(qardl_results, x_names, tau)

Arguments

qardl_results

List of QARDL results for each quantile

x_names

Names of independent variables

tau

Vector of quantiles

Value

List with long-run and short-run multiplier matrices

Compute Regime-Specific Multipliers

Description

Compute Regime-Specific Multipliers

Usage

compute_regime_multipliers(model_result, decompose, regime_names)

Multi-Threshold Decomposition

Description

Decomposes a variable into multiple regimes based on thresholds.

Usage

decompose_multi_threshold(x, thresholds)

Arguments

x

Numeric vector

thresholds

Threshold values (must include 0)

Value

List with regime components and names

Decompose Variables into Positive and Negative Changes

Description

Decomposes time series into cumulative positive and negative partial sums.

Usage

decompose_variables(data, variables)

Arguments

data

Data frame

variables

Variables to decompose

Value

List with positive and negative components

Estimate Fourier ADF for given k

Description

Estimate Fourier ADF for given k

Usage

estimate_fadf(y, k, model, max_lag, criterion)

Estimate Fourier KPSS

Description

Estimate Fourier KPSS

Usage

estimate_fkpss(y, k, model)

Estimate MTNARDL Model

Description

Estimate MTNARDL Model

Usage

estimate_mtnardl(y, X, p, q, case)

Estimate NARDL Model

Description

Estimate NARDL Model

Usage

estimate_nardl(y, X, fourier, p, q, case = 3)

Arguments

y

Dependent variable

X

Regressor matrix (with decomposed variables)

fourier

Fourier terms

p

Lag for y

q

Lag for X

case

Model case

Value

List with estimation results

Estimate Quantile ARDL Model

Description

Estimates the Quantile ARDL model for a given quantile tau.

Usage

estimate_qardl(y, X, fourier, p, q, tau, case = 3)

Arguments

y

Dependent variable

X

Independent variables

fourier

Fourier terms

p

Lag for dependent variable

q

Lag for independent variables

tau

Quantile (0 < tau < 1)

case

Model case (1-5)

Value

List with estimation results

F-test for Linearity in Fourier ADF

Description

Tests H0: gamma1 = gamma2 = 0 (no Fourier terms needed)

Usage

fadf_f_test(y, model, k, p)

Arguments

y

Time series

model

Model specification

k

Fourier frequency

p

Number of lags

Value

List with F-statistic and p-value

Approximate P-value for Fourier ADF

Description

Approximate P-value for Fourier ADF

Usage

fadf_pvalue(t_stat, n, model)

Fit Fourier ADF Model

Description

Fit Fourier ADF Model

Usage

fit_fadf_model(dy, y_lag1, sin_term, cos_term, p, model)

Fourier Nonlinear ARDL Estimation

Description

Estimates Fourier Nonlinear ARDL models with asymmetric cointegration following Shin, Yu & Greenwood-Nimmo (2014).

Usage

fnardl(formula, data, decompose = NULL, max_p = 4, max_q = 4, max_k = 3,
       criterion = c("BIC", "AIC", "HQ"), case = 3, bootstrap = FALSE, 
       n_boot = 1000, verbose = TRUE)

Arguments

formula

A formula specifying the model.

data

A data frame containing the variables.

decompose

Character vector of variables to decompose into positive/negative. Default NULL.

max_p

Maximum lag order for dependent variable.

max_q

Maximum lag order for independent variables.

max_k

Maximum Fourier frequency.

criterion

Information criterion for selection ("BIC", "AIC", or "HQ").

case

PSS case (1-5), default is 3.

bootstrap

Logical. Whether to perform bootstrap bounds test.

n_boot

Number of bootstrap replications.

verbose

Logical. Print progress messages (default: TRUE).

Value

An object of class "fnardl" containing estimation results.

Author(s)

Muhammad Alkhalaf

References

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework.

Examples


  data(macro_data, package = "fqardl")
  result <- fnardl(gdp ~ oil_price, data = macro_data, decompose = "oil_price")
  summary(result)

Fourier ADF Unit Root Test

Description

Performs the Fourier Augmented Dickey-Fuller test for unit roots with smooth structural breaks.

Usage

fourier_adf(y, max_k = 3, max_lag = 8, criterion = c("BIC", "AIC"))

Arguments

y

Time series vector

max_k

Maximum Fourier frequency

max_lag

Maximum number of lags for ADF

criterion

Lag selection criterion

Value

A list with test results

============================================================================= Fourier Unit Root Tests Based on Enders & Lee (2012) and Becker, Enders & Lee (2006) Ported from Python: Dr. Merwan Roudane R implementation: Muhammad Alkhalaf (Rufyq Elngeh) ============================================================================= Fourier ADF Test

Description

Tests for unit roots allowing for smooth structural breaks using Fourier approximation. Implements Enders & Lee (2012) methodology.

Usage

fourier_adf_test(
  y,
  model = c("c", "ct"),
  max_freq = 3,
  max_lag = NULL,
  criterion = c("AIC", "BIC", "t-sig"),
  verbose = TRUE
)

Arguments

y

Numeric vector of time series data

model

Model specification: "c" (constant), "ct" (constant + trend)

max_freq

Maximum Fourier frequency to test (default: 3)

max_lag

Maximum lag for ADF (default: NULL, auto-select)

criterion

Lag selection criterion ("AIC", "BIC", "t-sig")

verbose

Logical. Print progress messages (default: TRUE)

Value

Object of class "fadf" with test results

References

Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-199.

Examples


set.seed(123)
y <- cumsum(rnorm(200))  # Random walk
result <- fourier_adf_test(y, model = "c", max_freq = 3)
print(result)

Fourier KPSS Test

Description

Tests for stationarity allowing for smooth structural breaks. Implements Becker, Enders & Lee (2006) methodology.

Usage

fourier_kpss_test(y, model = c("c", "ct"), max_freq = 3, verbose = TRUE)

Arguments

y

Numeric vector of time series data

model

Model specification: "c" (constant), "ct" (constant + trend)

max_freq

Maximum Fourier frequency (default: 3)

verbose

Logical. Print progress messages (default: TRUE)

Value

Object of class "fkpss" with test results

References

Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.

Complete Unit Root Analysis

Description

Performs both Fourier ADF and Fourier KPSS tests for comprehensive unit root analysis.

Usage

fourier_unit_root_analysis(y, name = "Series", max_freq = 3, verbose = TRUE)

Arguments

y

Time series

name

Optional name for the series

max_freq

Maximum Fourier frequency

verbose

Logical. Print progress messages (default: TRUE)

Value

List with results from both tests and joint conclusion

Fourier Quantile ARDL Estimation

Description

Estimates the Fourier Quantile Autoregressive Distributed Lag (FQARDL) model. This methodology extends QARDL by incorporating Fourier trigonometric terms to capture smooth structural breaks without prior knowledge of break timing.

Usage

fqardl(formula, data, tau = c(0.25, 0.5, 0.75), max_p = 4, max_q = 4, 
       max_k = 3, criterion = c("BIC", "AIC", "HQ"), case = 3, 
       bootstrap = FALSE, n_boot = 1000, seed = NULL, verbose = TRUE)

Arguments

formula

A formula of the form y ~ x1 + x2 + ...

data

A data frame containing the time series variables.

tau

Numeric vector of quantiles to estimate.

max_p

Maximum lag for dependent variable.

max_q

Maximum lag for independent variables.

max_k

Maximum Fourier frequency to test.

criterion

Information criterion for lag selection ("BIC", "AIC", "HQ").

case

Model case (1-5) following Pesaran et al. (2001).

bootstrap

Logical, perform bootstrap cointegration test.

n_boot

Number of bootstrap replications.

seed

Random seed for reproducibility.

verbose

Logical. Print progress messages (default: TRUE).

Value

An object of class "fqardl" containing:

coefficients

Estimated coefficients for each quantile

long_run

Long-run multipliers

short_run

Short-run multipliers

optimal_k

Optimal Fourier frequency

optimal_lags

Optimal lag structure

bounds_test

Results of bounds test for cointegration

diagnostics

Model diagnostics

Author(s)

Muhammad Alkhalaf

References

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships.

Examples


data(macro_data)
result <- fqardl(gdp ~ inflation + interest_rate, 
                 data = macro_data,
                 tau = c(0.25, 0.5, 0.75))
summary(result)

Generate FNARDL Report

Description

Generates a comprehensive report with all plots and tables.

Usage

generate_fnardl_report(
  obj,
  file = "fnardl_report.html",
  horizon = 20,
  verbose = TRUE
)

Arguments

obj

FNARDL object

file

Output file path (HTML or PDF)

horizon

Horizon for dynamic multipliers

verbose

Logical. Print completion message (default: TRUE)

Value

No return value, called for side effects (generates output or plots)

============================================================================= Fourier Approximation Functions For capturing smooth structural breaks ============================================================================= Generate Fourier Trigonometric Terms

Description

Creates sine and cosine terms for Fourier approximation of structural breaks. Based on Enders & Lee (2012) methodology.

Usage

generate_fourier_terms(n, k, cumulative = FALSE)

Arguments

n

Sample size (number of observations)

k

Fourier frequency (integer >= 1)

cumulative

If TRUE, includes all frequencies from 1 to k

Details

The Fourier terms are computed as:

sin(2\pi k t / T)

cos(2\pi k t / T)

where t is the time index and T is the sample size.

Value

A matrix with sine and cosine columns

Get Fourier ADF Critical Values

Description

Get Fourier ADF Critical Values

Usage

get_fadf_critical_values(n, model, k)

Get Fourier KPSS Critical Values

Description

Get Fourier KPSS Critical Values

Usage

get_fkpss_critical_values(model, k)

Get PSS Critical Values

Description

Returns critical values from Pesaran, Shin & Smith (2001) Table CI.

Usage

get_pss_critical_values(k, case = 3)

Arguments

k

Number of regressors (excluding the dependent variable)

case

Model case (1-5)

Value

Data frame with critical values

Simulated Macroeconomic Data with Structural Break

Description

A simulated quarterly dataset containing GDP, inflation, and interest rate with a structural break.

Usage

macro_data

Format

A data frame with 100 rows and 5 variables: gdp, inflation, interest_rate, oil_price, exchange_rate.

Source

Simulated data for package demonstration

Examples

data(macro_data)
head(macro_data)

Multi-Threshold Nonlinear ARDL

Description

Estimates Multi-Threshold NARDL models with multiple regime asymmetry.

Usage

mtnardl(formula, data, decompose = NULL, thresholds = NULL,
        max_p = 4, max_q = 4, criterion = c("BIC", "AIC", "HQ"), case = 3, verbose = TRUE)

Arguments

formula

A formula specifying the model.

data

A data frame containing the variables.

decompose

Variable to decompose (default NULL).

thresholds

List of threshold values (default NULL).

max_p

Maximum lag for dependent variable.

max_q

Maximum lag for independent variables.

criterion

Information criterion for selection.

case

PSS bounds test case.

verbose

Logical. Print progress messages (default: TRUE).

Value

An object of class "mtnardl".

Author(s)

Muhammad Alkhalaf

Newey-West Variance Estimator

Description

Newey-West Variance Estimator

Usage

newey_west_variance(resid, bandwidth)

Simulated Oil Price and GDP Data with Asymmetric Effects

Description

A simulated quarterly dataset where GDP responds asymmetrically to oil price changes.

Usage

oil_gdp_data

Format

A data frame with 200 rows and 3 variables: date, gdp, oil_price.

Source

Simulated data for package demonstration

Examples

data(oil_gdp_data)
head(oil_gdp_data)

============================================================================= Bounds Test for Cointegration Based on Pesaran, Shin & Smith (2001) With extensions for Quantile ARDL ============================================================================= Perform Bounds Test for Cointegration

Description

Performs the PSS (2001) bounds test for cointegration in the ARDL framework. Tests the joint significance of the lagged level variables.

Usage

perform_bounds_test(qardl_results, n, k, case = 3)

Arguments

qardl_results

List of QARDL estimation results

n

Sample size

k

Number of regressors

case

Model case (1-5)

Details

The five cases are:

Value

List with bounds test results

MTNARDL Bounds Test

Description

MTNARDL Bounds Test

Usage

perform_mtnardl_bounds(model_result, n, k, case)

NARDL Bounds Test

Description

NARDL Bounds Test

Usage

perform_nardl_bounds_test(nardl_result, n, k, case)

============================================================================= Visualization Functions for FNARDL Dynamic Multiplier Plots and Asymmetry Analysis ============================================================================= Plot FNARDL Results

Description

============================================================================= Visualization Functions for FNARDL Dynamic Multiplier Plots and Asymmetry Analysis ============================================================================= Plot FNARDL Results

Usage

## S3 method for class 'fnardl'
plot(
  x,
  type = c("asymmetry", "dynamic", "cumulative", "comparison"),
  variable = NULL,
  horizon = 20,
  ...
)

Arguments

x

An object of class "fnardl"

type

Type of plot

variable

Variable to plot

horizon

Horizon for dynamic multipliers

...

Additional arguments

Value

No return value, called for side effects (generates output or plots)

============================================================================= Visualization Functions for FQARDL Publication-ready plots ============================================================================= Plot FQARDL Results

Description

Creates various diagnostic and result plots for FQARDL models.

Usage

## S3 method for class 'fqardl'
plot(
  x,
  type = c("coefficients", "multipliers", "3d", "heatmap", "residuals"),
  variable = NULL,
  ...
)

Arguments

x

An object of class "fqardl"

type

Type of plot: "coefficients", "multipliers", "3d", "heatmap", "residuals"

variable

Variable name for coefficient plots

...

Additional arguments passed to plotting functions

Value

A ggplot object or plotly object for 3D plots

Plot 3D Surface of Coefficients

Description

Plot 3D Surface of Coefficients

Usage

plot_3d_surface(obj, variable = NULL)

Arguments

obj

FQARDL object

variable

Variable to plot

Value

plotly object

Plot Asymmetry Bar Chart

Description

Plot Asymmetry Bar Chart

Usage

plot_asymmetry(obj, variable = NULL)

Arguments

obj

FNARDL object

variable

Variable to plot

Plot Coefficients Across Quantiles

Description

Plot Coefficients Across Quantiles

Usage

plot_coefficients(obj, variable = NULL)

Arguments

obj

FQARDL object

variable

Variable name (NULL for all)

Value

ggplot object

Plot Cumulative Multipliers

Description

Plot Cumulative Multipliers

Usage

plot_cumulative_multipliers(obj, variable, horizon = 20)

Arguments

obj

FNARDL object

variable

Variable to plot

horizon

Number of periods

Value

No return value, called for side effects (generates output or plots)

Plot Dynamic Multipliers

Description

Plot Dynamic Multipliers

Usage

plot_dynamic_multipliers(obj, variable, horizon = 20)

Arguments

obj

FNARDL object

variable

Variable to plot

horizon

Number of periods

Value

No return value, called for side effects (generates output or plots)

Plot Heatmap of Coefficients

Description

Plot Heatmap of Coefficients

Usage

plot_heatmap(obj)

Arguments

obj

FQARDL object

Value

ggplot object

Plot Long-run and Short-run Multipliers

Description

Plot Long-run and Short-run Multipliers

Usage

plot_multipliers(obj)

Arguments

obj

FQARDL object

Value

ggplot object

Plot Persistence Profile

Description

Plots the persistence profile showing the adjustment path to long-run equilibrium after a shock.

Usage

plot_persistence(obj, horizons = 20)

Arguments

obj

FQARDL object

horizons

Number of periods for persistence profile

Value

ggplot object

Plot Positive vs Negative Comparison

Description

Plot Positive vs Negative Comparison

Usage

plot_pos_neg_comparison(obj)

Arguments

obj

FNARDL object

Plot Residual Diagnostics

Description

Plot Residual Diagnostics

Usage

plot_residuals(obj)

Arguments

obj

FQARDL object

Value

ggplot object

Quantile Wald Test for Coefficient Constancy

Description

Tests whether coefficients are constant across quantiles.

Usage

quantile_wald_test(qardl_results, coef_name)

Arguments

qardl_results

List of QARDL results

coef_name

Name of coefficient to test

Value

List with test results

Select Optimal Fourier Frequency

Description

Selects the optimal Fourier frequency k based on information criteria. Tests all frequencies from 1 to max_k and selects the one minimizing the chosen criterion.

Usage

select_fourier_frequency(y, X, max_k = 3, criterion = c("BIC", "AIC", "HQ"))

Arguments

y

Dependent variable vector

X

Matrix of independent variables

max_k

Maximum Fourier frequency to test

criterion

Information criterion ("AIC", "BIC", "HQ")

Value

A list containing:

optimal_k

The optimal Fourier frequency

ic_values

Information criterion values for each k

criterion

The criterion used

Select Optimal Lags for MTNARDL

Description

Select Optimal Lags for MTNARDL

Usage

select_mtnardl_lags(y, X, max_p, max_q, criterion)

============================================================================= Quantile ARDL Estimation Functions Based on Cho et al. (2015) and extensions ============================================================================= Select Optimal Lag Structure

Description

Selects optimal lag orders for ARDL model using grid search over all combinations of p and q.

Usage

select_optimal_lags(
  y,
  X,
  fourier,
  max_p,
  max_q,
  criterion = c("BIC", "AIC", "HQ")
)

Arguments

y

Dependent variable

X

Matrix of independent variables

fourier

Fourier terms matrix

max_p

Maximum lag for dependent variable

max_q

Maximum lag for independent variables

criterion

Information criterion

Value

List with optimal lags

Test for Asymmetry (Wald Test)

Description

Test for Asymmetry (Wald Test)

Usage

test_asymmetry(nardl_result, decompose)

Arguments

nardl_result

NARDL estimation results

decompose

Decomposed variables

Value

List of Wald test results for each variable

Test Regime Asymmetry

Description

Test Regime Asymmetry

Usage

test_regime_asymmetry(model_result, decompose, regime_names)

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