Xiu Xu, Weining Wang, Yongcheol Shin, Chaowen Zheng
arXiv 15 Nov 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2022) · 18 citations (OpenAlex)
arXiv:2111.07633 · PDF · DOI · OpenAlex · Extracted main text
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. (2019b) 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zhu, X., Wang, W., Wang, H., and Härdle, W. K (2019) Network quantile autoregression self | 0.928 | 4 | 4 | 100% |
| 2 | Xu, X. and Lee, L.-f (2015) Maximum likelihood estimation of a spatial autoregressive Tobit model self | 0.843 | 4 | 3 | 75% |
| 3 | Jenish, N. and Prucha, I. R (2012) On spatial processes and asymptotic inference under near-epoch dependence | 0.843 | 10 | 3 | 60% |
| 4 | Koenker, R. and Xiao, Z (2006) Quantile autoregression | 0.843 | 3 | 3 | 100% |
| 5 | Chernozhukov, V. and Hansen, C (2006) Instrumental quantile regression inference for structural and treatment effect models | 0.754 | 7 | 4 | 43% |
| 6 | Powell, J. L (1991) Estimation of monotonic regression models under quantile restrictions | 0.737 | 5 | 3 | 40% |
| 7 | Su, L. and Yang, Z (2011) Instrumental variable quantile estimation of spatial autoregression models | 0.737 | 3 | 2 | 100% |
| 8 | Zhu, X., Pan, R., Li, G., Liu, Y., and Wang, H (2017) Network vector autoregression | 0.737 | 3 | 2 | 100% |
| 9 | Jenish, N. and Prucha, I. R (2009) Central limit theorems and uniform laws of large numbers for arrays of random fields | 0.644 | 4 | 2 | 50% |
| 10 | Anton, M. and Polk, C (2014) Connected stocks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 58 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Limit Theorems for Network Data without Metric Structure | 0.405 | 1 | 1 |