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Spectrum Auction Revenue Modeling and Prediction by Virtue of Hierarchical A Posteriori Clusters in Unpredictable Spectrum Economics Open Access

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The electromagnetic spectrum is a crucial asset for wireless technology innovation and for the industry's economy. The significance of the telecommunications industry's capital expenditures increases with the spectrum acquisition value in its primary market due to the scarcity factor, which is further influenced by interdependencies between the state of wireless technology innovation, auction bidding behavior, and economics.In this dissertation research, spectrum auction-related databases are examined to construct a hierarchical data matrix and to propose an optimal auction revenue model. The model incorporates statistically derived deterministic and stochastic variables to develop a spectrum valuation methodology for its primary market. The research reveals that the underlying regulations, bidding behavior, and spectrum demands due to wireless technology advancement are effectively elucidated by the selected explanatory variables and are highly correlated with auction revenues.This study is the first to introduce a hierarchical modeling technique that incorporates a multilevel data matrix culminated from 3,995 licenses of 15 Federal Communications Commission spectrum auctions to enable economic valuation for future spectrum auctions. Furthermore, the results of this study can be applied to evaluate the significance of sunk costs, winner's curses, and associated cost synergies, which are economic implications of spectrum auctions.The research contributions are twofold. First, the hierarchical auction model can maximize spectrum valuation methodologies, thereby assisting spectrum regulators and the wireless industry. Second, the study compares the reproducibility of hierarchical and ordinary least squares modeling techniques to support adequate validation and their extensive utilization in academic disciplines.

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