Dynamic Network Quantile Regression Model

References

• Acemoglu, D., Ozdaglar, A., and Tahbaz-Salehi, A. (2015), “Systemic Risk and Stability in Financial Networks,” American Economic Review, 105, 564–608. DOI: 10.1257/aer.20130456.
   PubMed Web of Science ®Google Scholar
• Ando, T., Greenwood-Nimmo, M., and Shin, Y. (2021), “Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks,” Management Science, Forthcoming. DOI: 10.1287/mnsc.2021.3984.
   Google Scholar
• Anton, M., and Polk, C. (2014), “Connected Stocks,” Journal of Finance, 69, 1099–1127. DOI: 10.1111/jofi.12149.
   Web of Science ®Google Scholar
• Barabási, A. L., and Albert, R. (1999), “Emergence of Scaling in Random Networks,” Science, 286, 509–512. DOI: 10.1126/science.286.5439.509.
   PubMed Web of Science ®Google Scholar
• Bassett, G., and Koenker, R. (1978), “Asymptotic Theory of Least Absolute Error Regression,” Journal of the American Statistical Association, 73, 618–622. DOI: 10.1080/01621459.1978.10480065.
   Web of Science ®Google Scholar
• Betz, F., Hautsch, N., Peltonen, T. A., and Schienle, M. (2016), “Systemic Risk Spillovers in the European Banking and Sovereign Network,” Journal of Financial Stability, 25, 206–224. DOI: 10.1016/j.jfs.2015.10.006.
   Web of Science ®Google Scholar
• Bofinger, E. (1975), “Estimation of a Density Function using Order Statistics,” Australian Journal of Statistics, 17, 1–7.
   Google Scholar
• Chen, C. Y.-H., Härdle, W. K., and Okhrin, Y. (2019), “Tail Event Driven Networks of SIFIs,” Journal of Econometrics, 208, 282–298. DOI: 10.1016/j.jeconom.2018.09.016.
   Web of Science ®Google Scholar
• Chen, L.-Y., and Lee, S. (2018), “Exact Computation of GMM Estimators for Instrumental Variable Quantile Regression Models,” Journal of Applied Econometrics, 33, 553–567. DOI: 10.1002/jae.2619.
   Web of Science ®Google Scholar
• Chernozhukov, V., Fernandez-Val, I., and Weidner, M. (2020), “Network and Panel Quantile Effects via Distribution Regression,” Journal of Econometrics, Forthcoming. DOI: 10.1016/j.jeconom.2020.08.009.
   PubMedGoogle Scholar
• Chernozhukov, V., and Hansen, C. (2005), “An IV Model of Quantile Treatment Effects,” Econometrica, 73, 245–261. DOI: 10.1111/j.1468-0262.2005.00570.x.
   Web of Science ®Google Scholar
• Chernozhukov, V., and Hansen, C. (2006), “Instrumental Quantile Regression Inference for Structural and Treatment Effect Models,” Journal of Econometrics, 132, 491–525.
   Web of Science ®Google Scholar
• Chernozhukov, V., and Hansen, C. (2008), “Instrumental Variable Quantile Regression: A Robust Inference Approach,” Journal of Econometrics, 142, 379–398.
   Web of Science ®Google Scholar
• Cho, J. S., Kim, T.-h., and Shin, Y. (2015), “Quantile Cointegration in the Autoregressive Distributed-Lag Modeling Framework,” Journal of Econometrics, 188, 281–300. DOI: 10.1016/j.jeconom.2015.05.003.
   Web of Science ®Google Scholar
• Clauset, A., Shalizi, C. R., and Newman, M. E. (2009), “Power-Law Distributions in Empirical Data,” SIAM Review, 51, 661–703. DOI: 10.1137/070710111.
   Web of Science ®Google Scholar
• Diebold, F. X., and Yilmaz, K. (2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,” Journal of Econometrics, 182, 119–134. DOI: 10.1016/j.jeconom.2014.04.012.
   Web of Science ®Google Scholar
• Engle, R. F., and Manganelli, S. (2004), “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,” Journal of Business & Economic Statistics, 22, 367–381.
   Web of Science ®Google Scholar
• Fafchamps, M., and Gubert, F. (2007), “Risk Sharing and Network Formation,” American Economic Review, 97, 75–79. DOI: 10.1257/aer.97.2.75.
   Web of Science ®Google Scholar
• Firpo, S., Galvao, A. F., Pinto, C., Poirier, A., and Sanroman, G. (2021), “GMM Quantile Regression,” Journal of Econometrics, Forthcoming. DOI: 10.1016/j.jeconom.2020.11.014.
   Web of Science ®Google Scholar
• Forni, M., and Gambetti, L. (2010), “The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach,” Journal of Monetary Economics, 57, 203–216. DOI: 10.1016/j.jmoneco.2009.11.009.
   Web of Science ®Google Scholar
• Frölich, M., and Melly, B. (2013), “Unconditional Quantile Treatment Effects under Endogeneity,” Journal of Business & Economic Statistics, 31, 346–357.
   Web of Science ®Google Scholar
• Galvao, A. F., Montes-Rojas, G., and Park, S. Y. (2013), “Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns,” Oxford Bulletin of Economics and Statistics, 75, 307–321. DOI: 10.1111/j.1468-0084.2011.00683.x.
   Web of Science ®Google Scholar
• Garcia, D., and Norli, O. (2012), “Geographic Dispersion and Stock Returns,” Journal of Financial Economics, 106, 547–565. DOI: 10.1016/j.jfineco.2012.06.007.
   Web of Science ®Google Scholar
• Hall, P., and Sheather, S. J. (1988), “On the Distribution of a Studentized Quantile,” Journal of the Royal Statistical Society, Series B, 50, 381–391. DOI: 10.1111/j.2517-6161.1988.tb01735.x.
   Google Scholar
• Härdle, W. K., Wang, W., and Yu, L. (2016), “Tenet: Tail-Event Driven Network Risk,” Journal of Econometrics, 192, 499–513. DOI: 10.1016/j.jeconom.2016.02.013.
   Web of Science ®Google Scholar
• Hautsch, N., Schaumburg, J., and Schienle, M. (2014), “Forecasting Systemic Impact in Financial Networks,” International Journal of Forecasting, 30, 781–794. DOI: 10.1016/j.ijforecast.2013.09.004.
   Web of Science ®Google Scholar
• Hautsch, N., Schaumburg, J., and Schienle, M. (2015), “Financial Network Systemic Risk Contributions,” Review of Finance, 19, 685–738.
   Web of Science ®Google Scholar
• Holland, P. W., and Leinhardt, S. (1981), “An Exponential Family of Probability Distributions for Directed Graphs,” Journal of the American Statistical Association, 76, 33–50. DOI: 10.1080/01621459.1981.10477598.
   Web of Science ®Google Scholar
• Jenish, N., and Prucha, I. R. (2009), “Central Limit Theorems and Uniform Laws of Large Numbers for Arrays of Random Fields,” Journal of Econometrics, 150, 86–98. DOI: 10.1016/j.jeconom.2009.02.009.
   PubMed Web of Science ®Google Scholar
• Jenish, N., and Prucha, I. R. (2012), “On Spatial Processes and Asymptotic Inference under Near-Epoch Dependence,” Journal of Econometrics, 170, 178–190.
   PubMed Web of Science ®Google Scholar
• Kapetanios, G., Pesaran, M. H., and Reese, S. (2021), “Detection of Units with Pervasive Effects in Large Panel Data Models,” Journal of Econometrics, 221, 510–541. DOI: 10.1016/j.jeconom.2020.05.001.
   Web of Science ®Google Scholar
• Koenker, R., and Machado, J. A. (1999), “Goodness of Fit and Related Inference Processes for Quantile Regression,” Journal of the American Statistical Association, 94, 1296–1310. DOI: 10.1080/01621459.1999.10473882.
   Web of Science ®Google Scholar
• Koenker, R., and Xiao, Z. (2006), “Quantile Autoregression,” Journal of the American Statistical Association, 101, 980–990. DOI: 10.1198/016214506000000672.
   Web of Science ®Google Scholar
• Liu, X. (2014), “Identification and Efficient Estimation of Simultaneous Equations Network Models,” Journal of Business & Economic Statistics, 32, 516–536.
   Web of Science ®Google Scholar
• Machado, J. A., and Silva, J. S. (2019), “Quantiles via Moments,” Journal of Econometrics, 213, 145–173. DOI: 10.1016/j.jeconom.2019.04.009.
   Web of Science ®Google Scholar
• Nowicki, K., and Snijders, T. A. B. (2001), “Estimation and Prediction for Stochastic Blockstructures,” Journal of the American Statistical Association, 96, 1077–1087. DOI: 10.1198/016214501753208735.
   Web of Science ®Google Scholar
• Pesaran, M. H., and Yang, C. F. (2020), “Econometric Analysis of Production Networks with Dominant Units,” Journal of Econometrics, 219, 507–541. DOI: 10.1016/j.jeconom.2020.03.014.
   Web of Science ®Google Scholar
• Pesaran, M. H., and Yang, C. F. (2021), “Estimation and Inference in Spatial Models with Dominant Units,” Journal of Econometrics, 221, 591–615.
   Web of Science ®Google Scholar
• Pirinsky, C., and Wang, Q. (2006), “Does Corporate Headquarters Location Matter for Stock Returns?” Journal of Finance, 61, 1991–2015. DOI: 10.1111/j.1540-6261.2006.00895.x.
   Web of Science ®Google Scholar
• Powell, J. L. (1991), “Estimation of Monotonic Regression Models Under Quantile Restrictions,” in Nonparametric and Semiparametric Methods in Econometrics and Statistics, eds. W. A. Barnett, J. L. Powell, and G. E. Tauchen, pp. 357–384, Cambridge, UK: Cambridge University Press.
   Google Scholar
• Su, L., and Hoshino, T. (2016), “Sieve Instrumental Variable Quantile Regression Estimation of Functional Coefficient Models,” Journal of Econometrics, 191, 231–254. DOI: 10.1016/j.jeconom.2015.10.006.
   Web of Science ®Google Scholar
• Su, L., and Yang, Z. (2011), “Instrumental Variable Quantile Estimation of Spatial Autoregression Models,” Unpublished Manuscript, Singapore Management University.
   Google Scholar
• White, H., Kim, T.-H., and Manganelli, S. (2015), “VAR for VaR: Measuring Tail Dependence using Multivariate Regression Quantiles,” Journal of Econometrics, 187, 169–188. DOI: 10.1016/j.jeconom.2015.02.004.
   Web of Science ®Google Scholar
• Wüthrich, K. (2019), “A Closed-form Estimator for Quantile Treatment Effects with Endogeneity,” Journal of Econometrics, 210, 219–235. DOI: 10.1016/j.jeconom.2018.11.017.
   Web of Science ®Google Scholar
• Wüthrich, K. (2020), “A Comparison of Two Quantile Models with Endogeneity,” Journal of Business & Economic Statistics, 38, 443–456.
   Web of Science ®Google Scholar
• Xiao, Z. (2009), “Quantile Cointegrating Regression,” Journal of Econometrics, 150, 248–260. DOI: 10.1016/j.jeconom.2008.12.005.
   Web of Science ®Google Scholar
• Xu, X., and Lee, L.-f. (2015), “Maximum Likelihood Estimation of a Spatial Autoregressive Tobit Model,” Journal of Econometrics, 188, 264–280. DOI: 10.1016/j.jeconom.2015.05.004.
   Web of Science ®Google Scholar
• Zhao, Y., Levina, E., and Zhu, J. (2012), “Consistency of Community Detection in Networks Under Degree-Corrected Stochastic Block Models,” Annals of Statistics, 40, 2266–2292.
   Web of Science ®Google Scholar
• Zhu, X. (2020), “Nonconcave Penalized Estimation in Sparse Vector Autoregression Model,” Electronic Journal of Statistics, 14, 1413–1448. DOI: 10.1214/20-EJS1693.
   Web of Science ®Google Scholar
• Zhu, X., Chang, X., Li, R., and Wang, H. (2019a), “Portal Nodes Screening for Large Scale Social Networks,” Journal of Econometrics, 209, 145–157. DOI: 10.1016/j.jeconom.2018.12.021.
   PubMed Web of Science ®Google Scholar
• Zhu, X., Huang, D., Pan, R., and Wang, H. (2020), “Multivariate Spatial Autoregressive Model for Large Scale Social Networks,” Journal of Econometrics, 215, 591–606. DOI: 10.1016/j.jeconom.2018.11.018.
   Web of Science ®Google Scholar
• Zhu, X., and Pan, R. (2020), “Grouped ‘Network Vector Autoregression,” Statistica Sinca, 30, 1437–1462.
   Web of Science ®Google Scholar
• Zhu, X., Pan, R., Li, G., Liu, Y., and Wang, H. (2017), “Network Vector Autoregression,” Annals of Statistics, 45, 1096–1123.
   Web of Science ®Google Scholar
• Zhu, X., Wang, W., Wang, H., and Härdle, W. K. (2019b), “Network Quantile Autoregression,” Journal of Econometrics, 212, 345–358. DOI: 10.1016/j.jeconom.2019.04.034.
   Web of Science ®Google Scholar