AbstractBanking crises are occurring intermittently. This indicates that pre-current warning models have not been successful in identifying these crises. Examination of existing models specifies that the failure of these models is mainly due to the identification of explanatory variables and experimental design of the model, which the researchers of the present study aimed at improving. In order to moderate the problem of model uncertainty by Averaging all models (Bayesian Averaging) the present research attempted to determine the factors affecting the banking crisis in Iran. In this study, 49 variables affecting the banking crisis were included in the model. Finally, using the Bayesian Averaging model Approach, 12 non-fragile variables affecting the financial crisis were identified consisting of cost of funding, none performing loan (NPL), deposit to loan (DTL), spread, capital adequacy, earning assets to total assets ratio, net LTD (after deducted Legal reserves), cash coverage ratio, net stable funding ratio (NSFR) in the presence of all variables, duration of assets and liabilities, interest rate duration, and increase in properties' possession. According to the results, it could be deduced that the banking crisis index in the Iranian economy is a problem with wide dimensions as the variables related to monetary and financial sector policy makers affect this index. The banks studied in this study are 10 banks listed on the Tehran Stock Exchange (Kar Afarin, Eghtesad-e Novin, Parsian, Sina, Mellat, Tejarat, Saderat, Post Bank, Mellat, Dey) in an 11-year period from 2008 to 2019.

The identification of the most important factors affecting energy intensity with the aim of controlling and managing energy consumption is an important topic. Findings of different empirical studies on the factors affecting energy intensity are inconsistent and this raises uncertainty about the employed models. One of the techniques that conform to these uncertainty conditions of the model is the Bayesian Averaging Approach. The purpose of this study is to identify robust and fragile factors affecting energy intensity in Iran provinces over the period from 2008 till 2015 using Bayesian Averaging Approach. The studied variables are selected from among the economic, demographic, industrial, commercial, transportation, Energy sector, factors related to Knowledge-based economy and climate factors. 24 variables were reviewed and by assessment of more than 8 million regressions and Bayesian Averaging of the coefficients, 9 variables were identified as the most affecting factors on energy intensity in Iran provinces; share of service sector in production, ratio of export to production, share of oil and petroleum products in energy consumption, income per capita, energy price, number of warm months, per capita capital of employed persons, number of cold months and population growth rate. It was also revealed that per capita income, share of service sector in production, share of oil and petroleum products in energy consumption, energy price and number of warm months have negative effect on energy intensity but other robust variables increase energy intensity. These findings can provide important policy recommendations, especially for the use of energy planners and policy makers.

The present study represents an attempt to examine the main macro determinants of stock prices in OPEC oil exporting and importing countries in the study period 1996 to 2016 using Bayesian model Averaging (BMA). The oil importers in this study are the United States, Britain and Japan, and three countries, Iran, Saudi Arabia and Kuwait, have been selected as oil exporters. The findings of this study are that to predict and evaluate the stock price index for oil-importing countries, the three variables of exchange rate index, consumer price index and economic growth should be given more importance than other variables, while for oil-exporting countries, The three variables of broad money growth, exchange rate and import are the most important variables that should be considered. For oil-importing countries, among the macro variables studied, OPEC oil prices have a completely negative relationship with the stock price index of those countries, but in oil-importing countries, the gold price has a completely inverse relationship with the stock price index.

Introduction: Identify of factors that influence on health costs can be useful in determine the best policy to control and manage the health costs. Previous studies in this area has been done with assumption the certainty of model; While the lack of attention to the problem of model uncertainty can lead to bias and lack of performance in estimation of parameters that result is inappropriate forecasts and incorrect statistical inference. So, the main objective of this study is identify the robust determinants of health sector costs in Iran under uncertainty of model.Methods: This study uses the statistical data of 22 variables that affect health sector costs based on theoretical and empirical studies, is paid to identify the robust determinants of these costs in Iran during 1979-2013. For this purpose is used the Bayesian Averaging of Classical Estimates (BACE) Approach (due to favorable characteristics for the assumption of model uncertainty). Also, the statistical analyzes were performed using the R software.Results: estimation of 40000 regression and Bayesian Averaging from the coefficients shows that per capita income with the possibility of 0.98 and coefficient of 0.70, urbanization rate with the possibility of 0.93 and coefficient of 1.25, per capita public health costs with the possibility of 0.83 and coefficient of 0.29, dependency ratio with the possibility of 0.50 and coefficient of 0.27, physician per capita with the possibility of 0.49 and coefficient of 0.20 and the unemployment rate with the possibility of 0.38 and coefficient of -0.07, are non-fragile and robust variables.Conclusion: The results indicate that the most important determinants of health sector costs in Iran are respectively: per capita income, urbanization rate, per capita public health costs, dependency ratio, physician per capita and unemployment rate. The effect of all these variables on per capita health sector costs in the long run are sure and strong.

This paper examines the robust determinants of public sector size (government) in IRAN in the years 1979-2012 and the uncertainty condition of the model. For this purpose is used the Bayesian Averaging of Classical Estimates (BACE) Approach due to favorable characteristics for the assumption of model uncertainty. With estimate 16,000 regression and Bayesian Averaging from the coefficients, effective variables were identified. The results show that robust determinants of the public sector size were as follows: lag the government size, population, growth rate of oil revenues, the ratio of population less than 15 and over 65 years to total population, bureaucracy and economic globalization.

Background and Purpose: Genomic selection is used to select candidates for breeding programs for organisms. In this study, we use the Bayesian model Averaging (BMA) method for genomic selection by considering the skewed error distributions. Materials and Methods: In this study, we apply the BMA method to linear regression models with skew-normal and skew-t distributions to determine the best subset of predictors. Occam’s window and Markov-Chain Monte Carlo model composition (MC3) were used to determine the best model and its uncertainty. The Rice SNP-seek database was used to obtain real data, which included 152 single nucleotide polymorphisms (SNPs) with 6 phenotypes. Results: Numerical studies on simulated and real data showed that, although Occam’s window ran faster than the MC3 method, the latter method suggested better linear models for the data with both skew-normal and skew-t error distributions. Conclusion: The MC3 method performs better than Occam’s window in identifying the linear models with greater accuracy when dealing with skewed error distributions.

Introduction: Empirical evidence in many countries shows that several factors lead to the decision of individuals to attempt suicide. The aim of this study was to determine the social and economic factors affecting the suicide rate in 31 provinces of the country between 2012 and 2017. Methods: In the present study, the effect of 16 socio-economic explanatory variables on suicide rate was investigated using the Bayesian model mean (BMA) method in an analytical-descriptive manner using R statistical software. All required statistics and information have been collected from library sources and statistical yearbooks of the Statistics Center of Iran, the Civil Registration Organization and the Forensic Medicine Organization of the country. Results: In this study, it was found that in Iran in the study period, the fertility rate with a probability of presence of 0. 997 with a coefficient of 4. 972 has a very strong effect on the suicide rate. Also, the abortion rate variable with a probability of 0. 935 with a coefficient of 0. 201 is the second strongest variable affecting the suicide rate. Among the 4 independent economic variables, only the unemployment rate with a probability of 0. 719 and a coefficient of 0. 393 is known as the third variable affecting the suicide rate. The illiteracy rate variable with a probability of 0. 758 also has a relatively acceptable effect on the suicide rate. In contrast, other social and economic variables do not have much effect on the suicide rate in Iran. Conclusion: Based on the results of this study, fertility rate, abortion rate, unemployment rate and illiteracy rate are the four main factors affecting the suicide rate in Iran.

Nowadays, human development play a key role in Development studies. The man is the main pillar of sustainable Development. Human Development Index has been the most popular indicator of development.Knowing the variables involved in the estimation of human development index is essential in choosing the appropriate model that can accurately measure the human development index. In this paper, we consider the uncertainty modeling framework for study of the factors that affect the human development index. For this purpose, we use the Bayesian model Averaging which is appropriate with the assumption of model uncertainty. In this study, by estimating 960000 regression equations, five variables are identified as nonfragile variables which is mentioned as follows: Growth of oil revenues, growth of Government health expenditure, Growth of primary education, Inflation, capital stock. Other variables lost their effect in the presence of non-fragile variables. Therefore, it can be concluded that in order to raise the index of human development in the country, it is necessary to pay more attention to the mentioned variables.

The wide range of factors influencing growth in theoretical foundations and empirical studies and the weakness of conventional methods have led studies to focus on only one aspect of theoretical and empirical growth patterns. This gives rise to uncertainty about specifying or combining variables in the model and estimated coefficients. This uncertainty can lead to bias and inefficiency in estimating the coefficients resulting in inaccurate predictions and inaccurate statistical inference. Therefore, in this study, using the Bayesian Averaging method, the influence of the most important factors affecting the growth of Iranian agricultural sector during 1978-2017 was investigated. Using this Approach, all possible sub-models are estimated using study variables and then the coefficient of each variable is averaged across the models. The weights in this Averaging are determined by the Bayesian rule or the posterior probability of each pattern. In this study, 2048 different models were estimated. The results showed that investment, financial development and oil revenues with the probability of impact of 0. 81, 0. 67 and 0. 42, respectively, are the most important variables affecting the growth of agricultural sector and also the growth rate of agricultural imports with a probability of impact of 0. 90 had the most negative effect on the growth of value added. Therefore, investing and financing producers, paying attention to domestic production and setting trade policies on imports should be a top priority in policy making and planning.