Tobit Regression

In economies where banks play a key role in aggregating savings and allocating credit to various sectors, it is crucial to evaluate the performance of the banking system using appropriate methods. This research paper presents a model for evaluating the efficiency of commercial banks listed in the Tehran Stock Exchange during the period from 2015 to 2020, with a focus on the impact of credit risk. The study employs a two-step descriptive-correlation retrospective method to rank the banks and explain the role of credit risk in their efficiency. Specifically, the efficiency of the banks is determined using inputs and outputs based on DEA (Data Envelopment Analysis) models. The calculation of efficiency using ideal SBM (Slacks-Based Measure) and DEA methods reveals that Mellat, Saderat, and Tejaret banks were the most efficient during the study period. Furthermore, Tobit and logistic regression models are used to investigate the relationship between the main determinants of credit risk and the efficiency of commercial banks. The findings indicate a statistically significant relationship between the two factors. Overall, this paper highlights the importance of evaluating the efficiency of the banking system in bank-oriented economies and provides a useful model for doing so. The research paper highlights the significant impact of credit risk on bank efficiency, emphasizing its role in shaping effective risk management strategies within the banking sector. It suggests that banks should prioritize these factors to enhance their operational efficiency.