Estimating the Fuzzy regression panel data model based on approximate Bayesian computation with application
DOI:
https://doi.org/10.62933/jt7pmy71Keywords:
Fuzzy Fixed Effect , Tanaka Fuzzy Fixed Effect, Quadratic Fuzzy Fixed Effect, Fuzzy Fixed Effect Least Absolute , approximate Bayesian computationAbstract
This study aims to estimate the parameters of fuzzy regression for panel data using a fixed regression model. To achieve greater accuracy in estimation, an approach is proposed that combines two estimation methods, leveraging the advantages of both probabilistic and traditional methods. The fixed regression model provides an integrated framework for analyzing cross-sectional and temporal data, contributing to a comprehensive analysis of panel data. This approach was applied to water pollution data of the Euphrates River using the fuzzy fixed regression model. The root mean square error (RMSE) criterion was used to compare different estimation methods. The results showed that the proposed methods generally outperformed the estimation methods, and gave better performance than probabilistic and traditional estimation methods. This study presents a new application for panel data using fuzzy regression, highlighting the benefit of combining traditional and probabilistic methods to achieve better estimations.
References
References
Abd, N. A., & Fadhil, N. H. (2019). The comparison between longitudinal data regression models in estimating and analyzing investment functions for productive economic sectors in Iraq for the period (1995-1996). Journal of Administration & Economics, 122.
Al-Adly, R. T. K., & Aboodi, E. H. (2021). Using some methods of estimating longitudinal data models with a practical application. Master’s Thesis-University of Baghdad, College of Administration and Economics.
Algamal, Z. Y. (2012). Selecting Model in Fixed and Random Panel Data Models. IRAOI JOURNAL OF STATISTICAL SCIENCES, 12(1).
Ali, A. H., Aljanabi, M., & Ahmed, M. A. (2020). Fuzzy generalized Hebbian algorithm for large-scale intrusion detection system. International Journal of Integrated Engineering, 12(1), 81–90.
Anand, M. C. J., & Bharatraj, J. (2017). Theory of triangular fuzzy number. Proceedings of NCATM, 80.
Arabpour, A. R., & Tata, M. (2008). Estimating the parameters of a fuzzy linear regression model. Iranian Journal of Fuzzy Systems, 5(2), 1–19.
Baltagi, B. H. (2005). Econometric Analysis of Panel Data, John Wiley&Sons Ltd. West Sussex, England.
Das, P. (2019). Econometrics in theory and practice. Springer, 10, 978–981.
Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy Sets and Systems, 3(1), 37–48.
Haggag, M. (2018). A new fuzzy regression model by mixing fuzzy and crisp inputs. Am Rev Math Stat, 6, 9–25.
Hassan, A. M., & Rahem, A. T. (2021). Comparison Of The Estimators Of The General Least Squares Method Of The Parametric Model With The Estimators Of The Nadaria-Watson Weighted And Unweighted Method Of The Non-Parametric Model Of Longitudinal Data. Iraqi Journal For Economic Sciences, 19(70).
Hsiao, C. (2003). Analysis of Panel Data. In Cambridge University Press (Second Edi).
Hsiao, C. (2014). Analysis of Panel Data. In Cambridge University Press (Third Edit). Cambridge University Press.
Kareem, R. E., & Mohammed, M. J. (2023). Fuzzy Bridge Regression Model Estimating via Simulation. Journal of Economics and Administrative Sciences, 29(136), 60–69.
Li, J., Zeng, W., Xie, J., & Yin, Q. (2016). A new fuzzy regression model based on least absolute deviation. Engineering Applications of Artificial Intelligence, 52, 54–64.
Wang, N., Reformat, M., Yao, W., Zhao, Y., & Chen, X. (2020). Fuzzy Linear regression based on approximate Bayesian computation. Applied Soft Computing, 97, 106763.
Wu, H.-C. (2003). Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data. Computational Statistics & Data Analysis, 42(1–2), 203–217.
Yalçın, M. O., Güler Dinçer, N., & Demir, S. (2021). Fuzzy panel data analysis. Kuwait Journal of Science, 48((3)), pp(1-13). https://doi.org/doi.org/10.48129/kjs.v48i3.8810
Yeylaghi, S., Otadi, M., & Imankhan, N. (2017). A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management. Beni-Suef University Journal of Basic and Applied Sciences, 6(2), 106–111.
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