Type: Package
Title: Nonparametric Causality in Quantiles Test
Version: 0.1.0
Author: Mehmet Balcilar [aut, cre]
Maintainer: Mehmet Balcilar <mehmet@mbalcilar.net>
Description: Implements the nonparametric causality-in-quantiles test (in mean or variance), returning a test object with an S3 plot() method. The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.
License: MIT + file LICENSE
URL: https://www.mbalcilar.net, https://github.com/mbalcilar/nonParQuantileCausality
Encoding: UTF-8
LazyData: true
LazyDataCompression: bzip2
RoxygenNote: 7.3.2
Depends: R (≥ 3.6)
Imports: stats, ggplot2, quantreg, KernSmooth
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-09-22 20:25:08 UTC; mbalcilar
Repository: CRAN
Date/Publication: 2025-09-30 07:20:08 UTC

nonParQuantileCausality: Nonparametric Causality in Quantiles

Description

Tools for nonparametric causality-in-quantiles tests (mean and variance) with an S3 plot method and an example dataset.

Lag order (important)

The current implementation uses one lag of each series (first-order Granger setup).

References

Author(s)

Maintainer: Mehmet Balcilar mehmet@mbalcilar.net

See Also

Useful links:


YourPackageName: Nonparametric Causality-in-Quantiles

Description

Tools for nonparametric causality-in-quantiles in mean and variance.

References


Monthly Gold and Oil Returns

Description

A small example dataset used to illustrate the nonparametric causality-in-quantiles test.

Usage

gold_oil

Format

A data frame with two numeric columns:

Gold

numeric: gold series

Oil

numeric: oil series

Details

Columns are generic numeric series (already aligned and cleaned) suitable for the examples in np_quantile_causality.

Source

Provided by Mehmet Balcilar.


Nonparametric Causality-in-Quantiles Test

Description

Computes the Balcilar-Jeong-Nishiyama style nonparametric quantile Granger-causality test for first-order lags. Methodology is based on Balcilar, Gupta, and Pierdzioch (2016, doi:10.1016/j.resourpol.2016.04.004) and Balcilar et al. (2016, doi:10.1007/s11079-016-9388-x).

Usage

np_quantile_causality(x, y, type = c("mean", "variance"), q = NULL, hm = NULL)

Arguments

x

numeric vector; candidate cause (independent) variable. The test internally uses the first lag of x (one-lag Granger causality setup).

y

numeric vector; effect (dependent) variable. The test internally uses the first lag of y (one-lag Granger causality setup).

type

character; "mean" or "variance" (causality in mean or variance).

q

numeric vector of quantiles in (0,1). Default is seq(0.01, 0.99, 0.01).

hm

optional numeric bandwidth; if NULL, uses Yu & Jones (1998) style plug-in via KernSmooth::dpill on the mean-regression proxy.

Details

Uses local polynomial quantile regression at each quantile with kernel weights, constructs the Song et al. (2012) style quadratic form, and rescales to the asymptotic standard-normal statistic.

Value

An object of class np_quantile_causality with elements:

Lag order (important)

The current implementation uses one lag of each series only: x_{t-1} and y_{t-1} (first-order Granger setup). Extending to higher lags requires changing the internal embedding (currently ⁠stats::embed(*, 2)⁠) and the kernel construction to handle multivariate lag vectors (e.g., a product kernel over all lag coordinates or a multivariate Gaussian kernel).

References

Note

This function tests whether x_{t-1} Granger-causes y_t in quantile \theta (and, with type = "variance", whether x_{t-1}^2 causes y_t^2). Higher-order lags are not supported in this release.

Examples


set.seed(1234)
x <- arima.sim(n = 600, list(ar = 0.4))
y <- 0.5*lag(x, -1) + rnorm(600)  # x Granger-causes y
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)  # test statistic vs quantiles with 5% CV line

# Example with bundled dataset (Gold causes Gold or Oil depending on call)
data(gold_oil)
# use first 500 days
gold_oil <- gold_oil[1:501,]
q_grid <- seq(0.25, 0.75, by = 0.25)

# Causality in conditional mean (does Oil_t-1 cause Gold_t?)
res_mean <- np_quantile_causality(
  x = gold_oil$Oil,
  y = gold_oil$Gold,
  type = "mean",
  q = q_grid
)
res_mean

# Causality in conditional variance
res_var <- np_quantile_causality(
  x = gold_oil$Oil,
  y = gold_oil$Gold,
  type = "variance",
  q = q_grid
)
res_var

# Plot (with 5% critical value line); returns a ggplot object invisibly
plot(res_mean)
plot(res_var)



Plot method for np_quantile_causality objects

Description

Plot method for np_quantile_causality objects

Usage

## S3 method for class 'np_quantile_causality'
plot(x, cv = 1.96, title = NULL, ...)

Arguments

x

an object of class np_quantile_causality

cv

numeric; a reference critical value line (default 1.96 for ~5%)

title

optional plot title; default is constructed from x$type

...

unused (for S3 compatibility)

Value

A ggplot object (invisibly).

References