Abstract
Panel data models have become increasingly popular in economic research and data analysis. Considering the uncertainty and variability of panel data, based on support vector regression, we propose robust estimations of some fixed effects panel interval-valued data models: nonlinear model, a special case of nonlinear model and nonlinear model with mathematical coherence. Monte Carlo simulations are used to evaluate the performance and robustness of our proposed models. The proposed models are applied to real datasets for stock price prediction, and experimental results demonstrate the excellent fitting and forecasting performance of our proposed models.
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The authors are grateful to the anonymous referees for their careful revision, valuable suggestions, and comments which improved this paper. The authors would like to thank the financial support from Natural Science Foundation Project of Hebei Province (A2022201002). The authors have no conflicts of interest to declare that are relevant to the content of this article.
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Ji, Ab., Li, Qq. & Zhang, Jj. Panel Interval-Valued Data Nonlinear Regression Models and Applications. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10519-8
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DOI: https://doi.org/10.1007/s10614-023-10519-8