Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Participation

05.12.2019. Wolfgang Karl Härdle, speaker of the IRTG 1792 gave a talk on 'Influencers and Communities in Social Networks' at the Institute of Statistical Science, Academia Sinica (中央研究院統計科學所究所), located in Taiwan. The abstract of the talk is given below:

Abstract

The integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter Θ typically much larger than the number of observations . To cope with this problem, we introduce a new structural mode SONIC which assumes that (1) a few influencers drive the network dynamics; (2) the community structure of the network is characterized as the homogeneity of response to the specific infuencer, implying their underlying similarity. An estimation procedure is proposed based on a greedy algorithm and LASSO regularization. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when the observed time interval is smaller than the size of the network . Using a novel dataset retrieved from a leading social media platform StockTwits and quantifying their opinions via StockTwits and quantifying their opinions via natural natural language processing, we model the opinions network language processing, we model the opinions network dynamics among a select group of users and further among a select group of users and further detect the latent communities. With a sparsity With a sparsity regularization, we can identify important nodes in the regularization, we can identify important nodes in the network.