A Comparative Study of Beta Regression Methods for Modeling Financial Ratios

Taha  Alshaybawee; Hamza Lateef Katea  Al-Ayashy; Ayat Salim  Al-Jajawi

Taha Alshaybawee Department of Statistics, College of administration and Economics, University of Al-Qadisiyah, Iraq
Hamza Lateef Katea Al-Ayashy Department of Statistics, College of administration and Economics, University of Al-Qadisiyah, Iraq
Ayat Salim Al-Jajawi Department of Statistics, College of administration and Economics, University of Al-Qadisiyah, Iraq, Jabir ibn Hayyan, Medical University, Iraq

Keywords: Beta Regression, Bayesian Inference, Frequentist estimation, MCMC

Abstract

This study aims to compare the performance of Frequentist and Bayesian Beta Regression methods using two real-world datasets: the Credit Dataset and the Company Financial Dataset. The comparison focuses on parameter estimation, interval interpretation, and predictive accuracy using confidence intervals, credible intervals, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) as evaluation criteria. Both models produced consistent results across key predictors, with the Bayesian approach offering more intuitive and informative uncertainty quantification through credible intervals. Additionally, the Bayesian method demonstrated slightly better predictive performance, as reflected in marginally lower RMSE and MAE values. Overall, the findings suggest that while both approaches are effective, the Bayesian Beta Regression provides enhanced interpretability and slightly improved accuracy, making it a valuable alternative to the traditional frequentist approach.

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