SONIC: SOcial Network analysis with Influencers and Communities
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DOI: 10.1016/j.jeconom.2021.02.008
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- Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
- Renault, Thomas, 2017.
"Intraday online investor sentiment and return patterns in the U.S. stock market,"
Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
- Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Post-Print hal-03205113, HAL.
- Tengyao Wang & Richard J. Samworth, 2018. "High dimensional change point estimation via sparse projection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 57-83, January.
- Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019.
"Network quantile autoregression,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
- Zhu, Xuening & Wang, Weining & Wang, Hangsheng & Härdle, Wolfgang Karl, 2016. "Network quantile autoregression," SFB 649 Discussion Papers 2016-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
- Pesaran, M. Hashem & Yang, Cynthia Fan, 2020.
"Econometric analysis of production networks with dominant units,"
Journal of Econometrics, Elsevier, vol. 219(2), pages 507-541.
- Pesaran, H. & Yang, Cynthia Fan, 2016. "Econometric Analysis of Production Networks with Dominant Units," Cambridge Working Papers in Economics 1678, Faculty of Economics, University of Cambridge.
- M. Hashem Pesaran & Cynthia Fan Yang, 2016. "Econometric Analysis of Production Networks with Dominant Units," CESifo Working Paper Series 6141, CESifo.
- Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019. "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, vol. 208(1), pages 282-298.
- Diebold, Francis X. & Yılmaz, Kamil, 2014.
"On the network topology of variance decompositions: Measuring the connectedness of financial firms,"
Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," NBER Working Papers 17490, National Bureau of Economic Research, Inc.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Working Papers 11-45, Federal Reserve Bank of Philadelphia.
- Francis X. Diebold & Kamil Yılmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," PIER Working Paper Archive 11-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," Koç University-TUSIAD Economic Research Forum Working Papers 1124, Koc University-TUSIAD Economic Research Forum.
- Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
- P. Čížek & W. Härdle & V. Spokoiny, 2009. "Adaptive pointwise estimation in time-inhomogeneous conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 248-271, July.
- Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021.
"Nonlinear factor models for network and panel data,"
Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
- Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Oct 2019.
- Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2018. "Nonlinear factor models for network and panel data," CeMMAP working papers CWP38/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2019. "Nonlinear factor models for network and panel data," CeMMAP working papers CWP18/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Chernozhukov, V. & Härdle, W.K. & Huang, C. & Wang, W., 2018.
"LASSO-Driven Inference in Time and Space,"
Working Papers
18/04, Department of Economics, City St George's, University of London.
- Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Chernozhukov, Victor & Härdle, Wolfgang Karl & Huang, Chen & Wang, Weining, 2018. "LASSO-Driven Inference in Time and Space," IRTG 1792 Discussion Papers 2018-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2018. "LASSO-driven inference in time and space," CeMMAP working papers CWP36/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Jason Parker & Donggyu Sul, 2016. "Identification of Unknown Common Factors: Leaders and Followers," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 227-239, April.
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Keywords
; ; ; ; ;JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
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