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Activity Number:
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241
- SPEED: Statistics in Social Sciences and Survey Research Part 1
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #322882
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Title:
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Dynamic Models for Corporate Competitiveness of Global Health Care Industry with Mixed Effects
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Author(s):
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Mingzhao Hu* and Lingdi Zhao and Danlei Feng
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Companies:
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University of California, Santa Barbara and Ocean University of China and Ocean University of China
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Keywords:
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Dynamic models;
Healthcare industry;
Mixed effects;
Online predictions;
Corporate competitiveness;
COVID-19 repercussions
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Abstract:
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Corporate competitiveness measures the comprehensive ability to deliver healthcare services and therapeutics for individual firms. We aim to uncover dynamic relationships and perform online predictions for timely decisions and innovations. Given the longitudinal nature of the key factors for corporate competitiveness, a dynamic modeling approach of mixed effects state space models (MESSM) for multivariate longitudinal variables is proposed to explore the associations in real time while incorporating the unobserved underlying influences and considering for within and across corporation variability in the global healthcare industry. This framework has outstanding interpretability and flexibility by allowing for building the assumptions of the evolutions into the structure. Using data collected from stock exchanges since 2007, we report time-varying patterns and discover recent divergences. In applications, we suggest an aggressive corporate strategy based on strong long-term investments for a randomly selected company. Our study not only sheds light on the economic repercussions of COVID-19 on individual corporations, but also builds a novel pipeline with possibility for extensions.
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Authors who are presenting talks have a * after their name.
