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

Author

Listed:
  • Pinshan Pan

    (Boston College)

  • Heejoon Han

    (Sungkyunkwan University)

  • Gyure Kim

    (Sungkyunkwan University)

Abstract

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 pre- dictability. 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.

Suggested Citation

  • Pinshan Pan & Heejoon Han & Gyure Kim, 2026. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability across time-series and cross-sectional data," Stata Journal, StataCorp LLC, vol. 26(2), pages 244-273, June.
    • Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:244-273
    DOI: 10.1177/1536867X261449935
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