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Dynamic Network Quantile Regression Model

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  • Xiu Xu
  • Weining Wang
  • Yongcheol Shin
  • Chaowen Zheng

Abstract

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016.

Suggested Citation

  • Xiu Xu & Weining Wang & Yongcheol Shin & Chaowen Zheng, 2024. "Dynamic Network Quantile Regression Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 407-421, April.
    • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:2:p:407-421
    DOI: 10.1080/07350015.2022.2093882
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    Cited by:

    1. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.

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