The cross-quantilogram: Measuring quantile dependence and testing directional predictability across time-series and cross-sectional data

In this article, we introduce the package crossq , a user-friendly tool for estimating and visualizing the cross-quantilogram, which is a method that captures quantile dependence between two series and tests for directional predictability. The package includes three core commands: crossq_main estimates the cross-quantilogram coefficients using unconditional or conditional approaches; crossq_qstat performs quantile-based directional predictability tests using Box- Pierce and Ljung-Box-type Q statistics; and crossq_plot visualizes the results with confidence intervals or heatmaps across quantile combinations. Additional features include the partial cross-quantilogram, stationary bootstrap inference, and flexible customization options for estimation, testing, and visualization. These make the package suitable for a wide range of empirical applications in both time- series and cross-sectional contexts.