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Description

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) <doi:10.1016/0304-4076(94)01616-8>, Fourier-based tests with single frequency (Enders and Jones, 2016) <doi:10.1515/snde-2014-0101> and cumulative frequencies (Nazlioglu et al., 2019) <doi:10.1080/1540496X.2018.1434072>, quantile causality tests (Cai et al., 2023) <doi:10.1016/j.frl.2023.104327>, and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) <doi:10.1007/s12076-020-00263-0>. For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) <doi:10.1007/s11356-020-10092-9> and Panel Quantile Causality tests (Wang and Nguyen, 2022) <doi:10.1080/1331677X.2021.1952089>, as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) <doi:10.1080/07474938.2023.2178141>. 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

Metadata

Version

1.1.1

License

Unknown

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