Estimation and inference methods for the cross-quantilogram.
The cross-quantilogram is a measure of nonlinear dependence between
two variables, based on either unconditional or conditional quantile
functions. It can be considered an extension of the correlogram,
which is a correlation function over multiple lag periods that mainly
focuses on linear dependency. One can use the cross-quantilogram to
detect the presence of directional predictability from one time series
to another. This package provides a statistical inference method
based on the stationary bootstrap. For detailed theoretical and
empirical explanations, see Linton and Whang (2007) for univariate
time series analysis and Han, Linton, Oka and Whang (2016) for
multivariate time series analysis. The full references for these key
publications are as follows: (1) Linton, O., and Whang, Y. J. (2007).
The quantilogram: with an application to evaluating directional
predictability. Journal of Econometrics, 141(1), 250-282
The quantilogram package provides estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency.
This package allows users to detect the presence of directional predictability from one time series to another and provides a statistical inference method based on the stationary bootstrap.
You can install the released version of quantilogram from CRAN with:
install.packages("quantilogram")
Here's a basic example of how to use the quantilogram package:
library(quantilogram)
# Load example data
data("sys.risk")
# Select two variables
D = sys.risk[, c("JPM", "Market")]
# Set parameters
k = 1 # lag order
vec.q = seq(0.05, 0.95, 0.05) # a list of quantiles
B.size = 200 # Repetition of bootstrap
# Compute and plot cross-quantilogram
res = heatmap.crossq(D, k, vec.q, B.size)
# Display the plot
print(res$plot)
For more detailed examples and function descriptions, please refer to the package documentation.
The methods implemented in this package are based on the following key publications:
Linton, O., and Whang, Y. J. (2007). The quantilogram: With an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282. doi:10.1016/j.jeconom.2007.01.004
Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270. doi:10.1016/j.jeconom.2016.03.001
This package is free and open source software, licensed under GPL (>= 3).