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Description

Fourier Bootstrap ARDL Cointegration Test.

Implements the Fourier Bootstrap Autoregressive Distributed Lag (FBARDL) bounds testing approach for cointegration analysis. Combines the Pesaran, Shin & Smith (2001) <doi:10.1002/jae.616> ARDL bounds testing framework with Fourier terms to capture structural breaks following Yilanci, Bozoklu & Gorus (2020) <doi:10.1080/00036846.2019.1686454>, and bootstrap critical values based on McNown, Sam & Goh (2018) <doi:10.1080/00036846.2017.1366643> and Bertelli, Vacca & Zoia (2022) <doi:10.1016/j.econmod.2022.105987>. Features include automatic lag selection via AIC/BIC, optimal Fourier frequency selection by minimum SSR, long-run and short-run coefficient estimation, diagnostic tests, and dynamic multiplier analysis.

fbardl: Fourier Bootstrap ARDL Cointegration Test

R implementation of the Fourier Bootstrap ARDL (FBARDL) bounds testing approach for cointegration analysis.

Overview

The fbardl package combines:

  • Pesaran, Shin & Smith (2001) ARDL bounds testing framework
  • Fourier terms to capture smooth structural breaks (Yilanci et al., 2020)
  • Bootstrap critical values for robust inference (McNown et al., 2018; Bertelli et al., 2020)

Installation

# Install from source
install.packages("fbardl_1.0.0.tar.gz", repos = NULL, type = "source")

# Or using devtools
devtools::install_github("muhammedalkhalaf/fbardl")

Usage

library(fbardl)

# Load example data
data(fbardl_data)

# Basic Fourier ARDL test
result <- fbardl(y ~ x1 + x2, data = fbardl_data, type = "fardl")
summary(result)

# Bootstrap ARDL (McNown approach)
result_boot <- fbardl(y ~ x1 + x2, data = fbardl_data,
                      type = "fbardl_mcnown", reps = 999)
summary(result_boot)

# Without Fourier terms
result_nofourier <- fbardl(y ~ x1 + x2, data = fbardl_data, fourier = FALSE)

Test Types

TypeDescription
"fardl"Fourier ARDL with PSS bounds test (default)
"fbardl_mcnown"Bootstrap ARDL (McNown, Sam & Goh, 2018)
"fbardl_bvz"Bootstrap ARDL (Bertelli, Vacca & Zoia, 2020)

Features

  • Automatic lag selection via AIC or BIC
  • Fourier frequency selection by minimum SSR
  • Three cointegration tests: F-overall, t-dependent, F-independent
  • Degenerate case detection (McNown et al., 2018)
  • Long-run coefficient estimation via delta method
  • Diagnostic tests: Jarque-Bera, Breusch-Godfrey, Breusch-Pagan, ARCH

Output

The function returns an object of class "fbardl" containing:

  • Model coefficients and standard errors
  • Long-run and short-run coefficient estimates
  • Cointegration test statistics and p-values
  • Diagnostic test results
  • Model fit statistics (R², AIC, BIC)

References

  • Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616

  • McNown, R., Sam, C. Y., & Goh, S. K. (2018). Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50(13), 1509-1521. https://doi.org/10.1080/00036846.2017.1366643

  • Yilanci, V., Bozoklu, S., & Gorus, M. S. (2020). Are BRICS countries pollution havens? Evidence from a bootstrap ARDL bounds testing approach with a Fourier function. Sustainable Cities and Society, 55, 102035. https://doi.org/10.1016/j.scs.2020.102035

  • Kripfganz, S., & Schneider, D. C. (2020). Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models. Oxford Bulletin of Economics and Statistics, 82(6), 1456-1481. https://doi.org/10.1111/obes.12377

Author

Dr. Merwan Roudane ([email protected])

License

GPL-3

Metadata

Version

1.0.2

License

Unknown

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