Dynamic Network Quantile Regression Model

Yongcheol Shin; Chaowen Zheng; Weining Wang; xiu xu

doi:10.1080/07350015.2022.2093882

Dynamic Network Quantile Regression Model

Yongcheol Shin, Chaowen Zheng, Weining Wang, xiu xu^*

^*Corresponding author for this work

Economics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	15
Journal	Journal of Business and Economic Statistics
DOIs	https://doi.org/10.1080/07350015.2022.2093882
Publication status	Published - 25 Jul 2022

Keywords

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

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10.1080/07350015.2022.2093882

https://arxiv.org/abs/2111.07633

New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks
Shin, Y. (Principal investigator), Chen, J. (Co-investigator) & Wang, W. (Co-investigator)
ECONOMIC AND SOCIAL RESEARCH COUNCIL (ESRC)
1/12/20 → 31/07/24
Project: Research project (funded) › Research

Cite this

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title = "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 commonfactors 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",

author = "Yongcheol Shin and Chaowen Zheng and Weining Wang and xiu xu",

year = "2022",

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day = "25",

doi = "10.1080/07350015.2022.2093882",

language = "English",

journal = "Journal of Business and Economic Statistics",

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T1 - Dynamic Network Quantile Regression Model

AU - Shin, Yongcheol

AU - Zheng, Chaowen

AU - Wang, Weining

AU - xu, xiu

PY - 2022/7/25

Y1 - 2022/7/25

N2 - 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 commonfactors 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.

AB - 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 commonfactors 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.

KW - Dynamic network quantile regression model;

KW - IVQR estimator;

KW - Quantile Connectedness;

KW - Simultaneous network endogeneity

U2 - 10.1080/07350015.2022.2093882

DO - 10.1080/07350015.2022.2093882

M3 - Article

SN - 0735-0015

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

ER -

Dynamic Network Quantile Regression Model

Abstract

Keywords

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Projects

New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks

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