Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Participation

11.03.2020. Wolfgang Karl Härdle, speaker of the IRTG 1792, gave a talk on "SONIC: Social Network with Influencers and Communities" at the Charles University, Prague.

The abstract of his 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.