Recent crises indicate the failure of early warning models. The research considers this failure to identify the explanatory variables and the empirical design of the model, the factors that this research seeks to improve. In this research, it is attempted to determine the factors affecting the financial crisis in Iranian economy by defining uncertainty in crisis models and using a conventional approach to Bayesian average. In this study, 62 variables affecting the financial crisis were introduced into the model. Finally, using the Bayesian averaging model, 12 non-critical variables that affect the financial crisis, which include deficit or surplus, unofficial exchange rate deviation from the official, inflation rate, ratio External debt to foreign assets of the Central Bank; Increasing coefficient of money (liquidity/ monetary base); Export to GDP ratio; Import to GDP; Government expenditure to GDP ratio; Budget deficit to GDP; Liquidity ratio to foreign assets of Central Bank; Rate of credit growth granted to the private sector and inflation squeeze. Regarding the output of the results, it can be stated that the financial crisis index in Iran's economy is a multi-dimensional problem, as variables related to financial policy, monetary policy and foreign exchange policy affect this index.

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.

This paper identifies determinants of economic growth in Iran, by using averaging methods and annual time series data from 1974 to 2012. The results indicate that ratio of oil revenue to GDP is the most important variable affecting economic growth. Also the second and third effective variables on growth are respectively ratio of imported capital and intermediate goods to GDP and labor force which lead to an increase in growth.Endogenous growth factors which are the factors contributing to formation of human capital, not possess a large role in growth process. Investments, especially government investment affects contrary to were expected. In fact, low quality, and productivity of investments and poor allocation reduced importance of investment’s quantity. The nature of Iran’s economy has not endogenous and dynamic features and predominantly, growth has been made by injecting of exogenous sources. Emphasis on formal and informal educational orientation in the quality of human capital instead of increasing in quantity of education is recommended.

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 investigates relationship of 16 variables on economic growth in Iran, using Bayesian model averaging (BMA) and annual time series data from 1961 to 2014. “Inclusion probability” indicates that investment ratio, population growth rate (with a negative sign), imported capital good growth, labor force growth, and imported intermediate good growth take the first to fifth rank regarding their effects on economic growth respectively. The relationship between energy consumption and growth in non-oil production due to the low probability of this variable in the model is insignificant. Thus saving energy policies are not a threat to economic growth in Iran.

Previous studies in the corruption literature have introduced numerous variables as the determinants of corruption. This articles aims to evaluate the robustness of potential determinants of corruption by addressing the model uncertainty and endogeneitry. The results derived from an instrumental variable Bayesian model averaging analysis indicate that based on the data of 123 countries, rule of law, with a posterior inclusion probability (PIP) of 1 and posterior mean of 0. 662 has the most important role in keeping corruption under control among 36 explanatory variables. Government effectiveness, with a PIP of 0. 964 and posterior mean of 0. 358 is another significant variable in curbing corruption. Also, with a PIP of 0. 965 and posterior mean of-0. 194 the Asia dummy variable tells that corruption is a serious problem in the Asia region. Further, confining the analysis to 95 developing countries reveals that rule of law with a PIP of 0. 999 and posterior mean of 0. 684 is the most critical variable in the fight against corruption.

The purpose of this paper is to determine the most significant leading indicators of the currency crisis in two groups of countries, countries with the floating exchange rate regimes and countries with the non-floating ones. A unique set data that covers 43 countries and their currency crises during 1999-2014 and 64 indicators are used. Moreover, estimation technique that is robust to model uncertainty, i. e. Bayesian model averaging is applied. Two types of the warning systems are used. Therefore, in the first system, warning variables of currency crisis are investigated while in the second ones, determinants of the exchange market pressure volatility are examined. Morever, these systems involve monitoring the evaluation of several leading indicators from the financial, political, structural, trade and other sectors. Overall, the results indicate that Oil price plays a pivotal role as a warning variable in countries with floating exchange rate regimes in both systems. Just the same, for the countries that experienced non-floating exchange rate regime prior to the crisis and during of it, changes in exchange market pressure index is a significant leading indicator of currency crises.

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.

Aim: The aim of the study is to examine the determinants of high-tech exports in developing countries during the period 1996 to 2013. One of the most important challenges of the empirical modeling is the selection of the potential variables that can be included in the econometric model, especially when there is a very wide range of explanatory variables. There is no acceptable way to solve this problem in the conventional econometric models. This article tries to fill this gap using Bayesian model averaging and WALS econometrics approaches.Method: In this article we studied the determinants of export for 24 developing countries during the period 1996 to 2013 based on Bayesian model averaging (BMA) and Weighted-Average Least Square (WALS) technique.Findings: The institutional quality (with an average coefficient of 2.12), human capital (0.85), the ratio of imports to GDP (0.04) and logarithm of GDP (1.87) have definite effects on the exports with inclusion probability of 100%. The sign of these coefficients are as expected. The human capital implies the creation of the endogenous knowledge. The knowledge-based capital is one of the key inputs for productive activities according to the endogenous growth theories so that more human capital improves the quality and productivity and ultimately leads to greater exports. A country which has a greater GDP, can supply more various products. Therefore, the trade greatly depends on the size of the country in terms of GDP. In fact, economies with higher income are more interested in specialization and sophistication of the products and have more trades. Import as an important channel of international knowledge spillover has a positive effect on the high-tech export. Import also affects the host country's export through various channels. In the long term, the countries can accelerate export sophistication through the dissemination of the foreign knowledge. Imports of intermediate and capital goods causes the transfer of the new technology into the country and reduce their production costs leading to high-tech exports. Some developing countries import large quantities of intermediate and tech-intensive goods and export them after simple assembling and processing the complex final products. The existence or establishment of appropriate institutions can empower the endogenous growth and productivity and therefore provide competitiveness and sustainable growth in a country's exports. Especially, improving the institutional quality and the rule of law increases the security of property rights and contracts enforcements and therefore creates a safe environment for the development of new markets, strengthening human capital, domestic research and development and information and communication technology. It also increases the rate of return of the capitals and the incentives for domestic and foreign investments through reducing the risk, and ultimately fortifies the competitiveness and exports. The ratio of capital to labor as well as research and development expenditure to GDP with the probability of 99% have negative effects on the high-tech exports. The weighted average of these two variables' coefficients are respectively -0.68 and -0.89. The increase of the ratio of capital to labor and research and development expenditure in the developing countries (under study) with poor production structures do not have expected positive effect on increasing the high-tech exports. If the necessary institutional infrastructures are not provided, the development of the physical capital and even the research and development expenditures encloses the country's natural resource-based and traditional industries. Other explanatory variables including the land area per capita, the real effective exchange rate, the population, the ratio of FDI to GDP and inflation due to the low inclusion probability do not affect the exports.Conclusion: The traditional trade theories lack essential potentials to explain the tech-intensive exports in the developing countries. However, the institutions, the efficient human capital, higher GDP, the openness and the easier access to the foreign knowledge and investment can explain the behavior of the tech-intensive exports in the developing countries, confirming the main hypothesis of this article. Of course good governance maybe not perfect proxy of Institutional quality that should be addressed in future researches.

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.