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Panel Interval-Valued Data Nonlinear Regression Models and Applications

Author

Listed:
  • Ai-bing Ji

    (Hebei University)

  • Qing-qing Li

    (Hebei University)

  • Jin-jin Zhang

    (Hebei University)

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.

Suggested Citation

  • Ai-bing Ji & Qing-qing Li & Jin-jin Zhang, 2024. "Panel Interval-Valued Data Nonlinear Regression Models and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2413-2435, October.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:4:d:10.1007_s10614-023-10519-8
    DOI: 10.1007/s10614-023-10519-8
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    References listed on IDEAS

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    1. Charles F. Manski & Elie Tamer, 2002. "Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol. 70(2), pages 519-546, March.
    2. Li Tao & Yuanjie Zhang & Maozai Tian, 2019. "Quantile Regression for Dynamic Panel Data Using Hausman–Taylor Instrumental Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1033-1069, March.
    3. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    4. Gloria González-Rivera & Wei Lin, 2013. "Constrained Regression for Interval-Valued Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 473-490, October.
    5. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    6. Liang-Ching Lin & Hsiang-Lin Chien & Sangyeol Lee, 2021. "Symbolic interval-valued data analysis for time series based on auto-interval-regressive models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 295-315, March.
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