Dynamic Network Quantile Regression Model

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.

KEYWORDS:

• Dynamic network quantile regression model
• IVQR estimator
• Quantile Connectedness
• Simultaneous network endogeneity

Supplementary Materials

The online supplement contains all technical proofs, additional simulation and application results, as well as the codes and data for the simulation and application.

Acknowledgments

We are most grateful for the insightful comments by the editor, the associated editor, and three anonymous referees, which substantially improved the article. All errors remains on our own.

Additional information

Funding

Xiu Xu’s research is supported by the Natural Science Foundation of China (Grant No. 71803140). Yongcheol Shin and Weining Wang’s research is partially supported by the ESRC (Grant Reference: ES/T01573X/1). Chaowen Zheng’s research is supported by the Royal Economic Society.