Package: quantilogram 3.1.1

quantilogram: Cross-Quantilogram

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 <doi:10.1016/j.jeconom.2007.01.004>; (2) 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>.

Authors:Tatsushi Oka [aut, cre], Heejon Han [ctb], Oliver Linton [ctb], Yoon-Jae Whang [ctb]