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nonParQuantileCausality

Implements the nonparametric causality-in-quantiles test.

Lag order: first-order only (uses \(x_{t-1}\) and \(y_{t-1}\)).

Install (dev)

# install.packages("devtools")
devtools::install_github("https://github.com/mbalcilar/nonParQuantileCausality")

Example

library(nonParQuantileCausality)
set.seed(1)
x <- arima.sim(n = 600, list(ar = 0.4))
y <- 0.5*dplyr::lag(x, 1) + rnorm(600)  # if dplyr present; otherwise build your own lag
y[is.na(y)] <- mean(y, na.rm = TRUE)

obj <- np_quantile_causality(x, y, type = "mean", q = seq(0.1, 0.9, 0.1))
plot(obj)

library(nonParQuantileCausality)
data(gold_oil)

obj <- np_quantile_causality(
  x = gold_oil$Oil, y = gold_oil$Gold,
  type = "mean", q = seq(0.05, 0.95, 0.05)
)
plot(obj)

References

• Balcilar, M., Gupta, R., & Pierdzioch, C. (2016). Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test. Resources Policy, 49, 74–80.

• Balcilar, M., Gupta, R., Kyei, C., & Wohar, M. E. (2016). Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Economies Review, 27(2), 229–250.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.