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Influencer Detection meets Network AutoRegression – Influential Regions in the Bitcoin Blockchain

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1
Author
Simon Trimborn Hanqiu Peng Ying Chen
Category
Financial
Date Posted
2022/09/26
Date Retrieved
2022/09/26
Date Revised
Date Written
2022/09/26
Description
Known as an active global virtual money network Bitcoin blockchain with millions of accounts has played an ever-growing important role in fund transition digital payment and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others cross-border. For a granular analysis we analyze if the transaction record size plays a role for the dynamics of the cross-border transactions in the network. With two-layer sparsity DINAR enables discovering 1) the active regions with influential impact on the global digital money network and 2) if changes in the transaction record size impact the dynamic evolution of Bitcoin transactions. We illustrate the finite sample performance of DINAR along with intensive simulation studies and investigate its asymptotic properties. In the real data analysis on Bitcoin blockchain from Feb 2012 to December 2021 we found that in the earlier years (2012-2016) network e
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JEL Classifications
C55 C58 C60 G17
Keywords
Bitcoin Blockchain Network Dynamics Two-Layer sparsity
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Pages
75
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URL
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4230241
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