Multiple Granger Causality Tests for Time Series and Panel Data

Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) , Fourier-based tests with single frequency (Enders and Jones, 2016) and cumulative frequencies (Nazlioglu et al., 2019) , quantile causality tests (Cai et al., 2023) , and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) . For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) and Panel Quantile Causality tests (Wang and Nguyen, 2022) , as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) . All tests include bootstrap inference for robust p-values.

caustests: Multiple Granger Causality Tests

Comprehensive suite of Granger causality tests for time series analysis, including:

1. Toda-Yamamoto (1995) - Standard Granger causality robust to integration order
2. Single Fourier Granger (Enders & Jones, 2016) - Captures smooth structural breaks
3. Single Fourier Toda-Yamamoto (Nazlioglu et al., 2016) - Combines TY with Fourier
4. Cumulative Fourier Granger (Enders & Jones, 2019) - Multiple Fourier frequencies
5. Cumulative Fourier Toda-Yamamoto (Nazlioglu et al., 2019)
6. Quantile Toda-Yamamoto (Cai et al., 2023) - Causality across quantiles
7. Bootstrap Fourier Granger in Quantiles (Cheng et al., 2021) - BFGC-Q

All tests include bootstrap inference for robust p-values.

Installation

# Install from CRAN (when available)
install.packages("caustests")

# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("muhammedalkhalaf/caustests")

Usage

library(caustests)

# Load example data
data(caustests_data)

# Test 1: Toda-Yamamoto test
result1 <- caustests(caustests_data, test = 1, nboot = 999)
print(result1)

# Test 3: Single Fourier Toda-Yamamoto
result3 <- caustests(caustests_data, test = 3, kmax = 3, nboot = 999)
summary(result3)

# Test 6: Quantile causality
result6 <- caustests(caustests_data, test = 6, 
                     quantiles = c(0.1, 0.25, 0.5, 0.75, 0.9),
                     nboot = 999)
print(result6)
plot(result6)

References

• Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.
• Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics & Econometrics, 20(4), 399-419.
• Nazlioglu, S., Gormus, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs). Energy Economics, 60, 168-175.
• Nazlioglu, S., Soytas, U., & Gormus, N. A. (2019). Oil prices and monetary policy in emerging markets. Emerging Markets Finance and Trade, 55(1), 105-117.
• Cai, Y., Chang, T., Xiang, Y., & Chang, H. L. (2023). Testing Granger causality in quantiles. Finance Research Letters, 58, 104327.
• Cheng, S. C., et al. (2021). Bootstrap Fourier Granger causality test in quantiles. Letters in Spatial and Resource Sciences, 14, 31-49.

Author

License

GPL-3