Purpose: Evaluating the credit of customers in credit institutions is one of the most important challenges of banks today. The lack of accurate evaluation of customers can lead to delayed maturity and ultimately burn the bank's claims and increase the credit risk. Overdue claims, which are known as non-current claims in the banking literature, are the result of credit risks. The banking and economic authorities always try to minimize the credit risk and non-current claims in order to avoid bankruptcy and its negative consequences. Bank credit is an efficient scale for calculating credit risks. But the judgment method is often used to determine the credit risk of customers. This type of validation and decision-making is not as real as it should be; it has little accuracy and actually causes problems for banks in the process of handing over facilities. The purpose of this article is to present a detailed framework for the validation of bank customers, which is done on a case-by-case basis for a number of recipients of facilities from Saderat Bank in the period of 2019-2021.Methodology: In this study, the censored multiple logistic regression technique is employed to rank customers based on creditworthiness and examine its impact on reducing non-current claims in Saderat Bank. The results obtained can facilitate the decision-making process regarding the credit rating of customers and the reduction of non-current claims in Saderat Bank. This purpose is fulfilled with discrete regression models in which the dependent variable selects binary values. A common approach in modeling binary choices is the use of multiple logistic probability models (censored regression), where it is assumed that there is an unobservable dependent variable (yi*) defined by a regression relationship.The statistical population of the present study includes the real customers in the Saderat Bank system who have received facilities and whose facilities are now in progress or have ended. The data on these customers cover the 2000 credit facilities of people who have referred to the bank. The data were collected from Saderat Bank branches during the period of 2019-2021. Also, the statistical sample of the research was divided into two categories of customers, creditworthy and non-creditworthy.Findings and discussion: Based on the estimation of the censored regression model, gender and age have a significant effect on the timely collection of loans. Women have performed better than men in repaying the loan, and, as age increases, the probability of not repaying the loan increases. The level of education and the repayment period also have positive effects on the timely collection of claims. Moreover, the amount of the granted loans and the interest rate of the loans have a negative and significant effect. Among the other investigated variables is the history of obtaining loans by the borrower, which has a positive and significant effect on the timely collection of loans. This factor can be a good sign of the eligibility of the borrower. The type of collateral deposited with the bank as a guarantee is another investigated variable with a significant impact on the non-repayment of loans. The loans obtained with property collaterals have better repayment conditions than joint collaterals. Finally, the average balance of the borrower when taking a loan has been found as an effective and significant factor in identifying his eligibility. On the other hand, the type of facility (capital or current) has no effect on timely collection or overdue claims.In addition, the results obtained from the censoring model for bad credit customers, in which the deferred class and the questionable access class are considered as dependent variables, show that the factors of gender, age and loan amount have positive and significant effects on the non-repayment of the loan. The repayment period has a negative and significant effect on the non-repayment of the loan. Also, the factors of the average balance of the applicant, the type of facility and the customer's work have no significant effect on the creditworthiness of bad credit customers of Saderat Bank. With these findings on the bank, it is better to identify bad customers and focus on other study variables such as loan repayment time, installment interval, number of installments, amount of each installment per month, loan extension, history of receiving loans, real estate collateral, and interest rate of loans.Conclusions and Policy Implications: Collection of the granted loan within the specified period of time points to the implementation of the correct methods and the use of resources to create the loans necessary for expanding economic activities and directing the bank resources to the correct investment locations. However, the implementation of inappropriate economic policies in the past years, the lack of financial disciplines, and the unfavorable economic conditions of the country have played a large role in the non-repayment of the granted loans. Therefore, the demographic and economic features of loan recipients are the key parameters for validation, playing a central role in managing non-current claims and the credit risk of banks. It is, thus, suggested that, at the micro-level in the technical and economic evaluation of the projects for granting loans, banks should take into account the trend of macroeconomic variables, so that loans can be given based on the relevant forecasts and in such a way that the original resources and expected profit of the loans can be returned. Expansion of knowledge and research in the field of customer validation and reduction of non-current claims of banks will have valuable results for organizations. Among them, one may mention the reduction of costs and the possibility of completing the money cycle in the bank and the country.

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The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards, which show the profitability of customers. Applying these scorecards together makes the loan decision making process for banks more confusing. This paper has an obvious and clean solution for facilitating the confusion of loan decision making process by combining the credit and profit scorecards through introducing a matrix sequential hybrid credit scorecard. The applicability of the introduced matrix sequential hybrid scorecard results are shown using data from an Iranian bank.

Regression analysis is done, traditionally, considering homogeneity and normality assumption for the response variable distribution. Whereas in many applications, observations indicate to a heterogeneous structure containing some sub-populations with skew-symmetric structure either due to heterogeneity, multimodality or skewness of the population or a combination of them. In this situations, one can use a mixture of skew-symmetric distributions to model the population. In this paper we considered the Bayesian approach of regression analysis under the assumption of heterogeneity of population and a skew-symmetric distribution for sub-populations, by using a mixture of skew normal distributions. We used a simulation study and a real world example to assess the proposed Bayesian methodology and to compare it with frequentist approach.

The purpose of this study is to analyze oil and tax revenues in the Iranian economy. Applying the stochastic dynamic general equilibrium approach, a model for the oil exporting economy was simulated. In order to recover the tax system, oil and tax revenues added to the model as a part of government’s revenue. The present study applies Dynare software with Matlab to estimate the model’s parameters by Bayesian statistical techniques based on Monte Carlo method with Markov chain in the form of Metropolis-Hastings algorithm. The period of study is from 1368 to 1396 in a quarterly setting. In order to analyze the shocks, two scenarios were designed. In the first scenario, it was assumed that the government has oil revenues and all oil revenues are spent by the government so that the government does not rely on tax revenues. In the second scenario, it is assumed that 40% of the government's oil revenues are injected into the Development Fund and a percentage of it, is allocated to facilitate the production sector, and the government mostly relies on the taxes. The results show that the tax and oil shock by reducing dependence on oil and reliance on tax revenues, has negative impact on macroeconomic variables in the short term, but in the long run, with increasing tax revenues, production and, consequently, investment, consumption, employment has increased. JEL classification: H21 C13، E17، G5

Objectives: Ordinary regression model is basically built for conditional mean to show the relationship between mean and some explanatory variables through a statistical model. Some assumptions like normality should be defined with an acceptable degree of confidence to correctly make inference from this type of model. If a measure other than mean is interested in or these assumptions are not satisfied due to outliers, this model will not be useful. However, quantile regression is robust to the outliers and is able to build a model for any quantiles (quartles, deciles and percentiles).Method: In this paper the application of quantile regression is illustrated in the context of mental health data.Conclusion: In our study, quantile regression findings have shown different relationship of age to mental health for men and women whereas these could not be achieved by mean regression at all.

‎The high-dimensional data analysis using classical regression approaches is not applicable, and the consequences may need to be more accurate. This study tried to analyze such data by introducing new and powerful approaches such as support vector regression, functional regression, LASSO and ridge regression. On this subject, by investigating two high-dimensional data sets (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

Among the aḥādīth (Arabic: أحادیث, singular: ḥadīth, meaning traditions and narrations, Arabic: رِوایات, romanized: riwayāt), there are those attributed to the Prophet ṣallā llāhu ʿalayhī wa-ʾālihī wa-sallam and Ahl al-Bayt (Arabic: أَهْل ٱلْبَیْت) ʿalayhim al-ṣalātu wal-salām that address doctrinal, philosophical, and mystical themes. These narrations have been cited in various forms across jurisprudential, narrative, exegetical, mystical, philosophical, theological, and ethical works. Among these is the narration “I spend the night with my Lord, and He feeds and drinks me” (Arabic: أبیتُ عند ربّی یُطعِمُنی ویَسْقینی, romanized: “Abītu ʿinda Rabbī yuṭʿimunī wa yasqīnī”). The research question is: What is meant by the status of “being near” (Arabic: عِندیَّة, romanized: ʿindīyya) and passing and spending the night with the Lord (Arabic: بَیْتوتَة, romanized: Baytūta), and the relation (object) of “eating and drinking” in the ḥadīth? This article employs a descriptive-analytical method to examine the authenticity, implications, and content of the mentioned ḥadīth. It clarifies that, based on rational and traditional evidence from credible narrations, this ḥadīth is rooted in rational principles and legal foundations, and is aligned with logical and consistent usages without the need for interpretation or hesitation. Although various opinions based on differing juridical interpretations have emerged, it can be concluded, in accordance with the principle of the spirit of meaning and the stipulation (position) of words to general meanings, that the ḥadīth conveys a profound mystical and philosophical understanding: that food and drink possess different material and spiritual levels. Whenever a person frees from worldly attachments and attains the Divine nearness (nearness to God) and “being near” (Arabic: عِندیَّة, romanized: ʿindīyya) to the Lord, their food and drink at that level become light and knowledge, rendering them independent of worldly sustenance.