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.

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.

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.

Purpose: One of the most important economic problems in Iran during the last few decades is the phenomenon of high and double-digit inflation. So, improving the conditions caused by high inflation has always been one of the important goals of the country's development programs. Achieving this goal requires the creation of a precise and targeted mechanism in the economic policy-making process. In its standard form, it includes forecasting, goal setting and policy analysis. The general purpose of this study is to predict the inflation rate using economic variables that affect it. In this research, the Bayesian averaging method is used to investigate the best estimation model that can predict the inflation in Iran. In this regard, the previous studies conducted in this field are first reviewed, and then the most important economic variables affecting the inflation are identified and used to predict the inflation rate. Methodology: Friedman believes that inflation is always and everywhere a monetary phenomenon. Monetarists believe that inflation comes from the disproportionate growth of nominal money supply. So, the higher this growth, the higher the inflation rate is. There is a direct and proportional relationship between money growth and inflation. According to this theory, changes in money supply have no effect on real variables such as production, employment and real wages,they only affect nominal variables such as prices and nominal wages proportionally. Monetarists consider the real growth of the economy in the long term to be independent of the change in the money supply and generally believe that this growth is determined by factors such as production capacity, increase in labor force due to population growth, advancement of technical knowledge and natural resources. In order to control or curb inflation, the influencing factors must be identified. The results of the studies on the factors that cause inflation are different or even inconsistent, because it is based on the researcher's specific attitude. In this article, to avoid falling into such a vortex, the Bayesian averaging method is used to predict inflation. The seasonal data of 1990-2022 have been used to predict the inflation rate in Iran. Results and discussion: Forecasting inflation is one of the most important but difficult issues in macroeconomics. Many different approaches have been proposed in this field. Perhaps the most popular of these approaches are those based on the Phillips curve. However, the general framework includes a dependent variable such as inflation (or change in inflation) and explanatory variables such as inflation breaks, unemployment rate and other predictive factors. Meanwhile, return and regression-based methods have been somewhat more successful. The results show that dynamic model averaging leads to significant improvements in forecasting compared to other approaches such as OLS, ARMA, and ARDL. Also, among the variables influencing inflation, the most influential for predicting the inflation rate relates to household consumption expenditure, unemployment rate, and workers' wage rate. Conclusions and policy implications: The general purpose of this study was to predict the inflation rate using the economic variables that affect it. In this research, the Bayesian averaging method served to investigate the best estimation model that can predict the inflation in Iran. In this regard, the previous studies conducted in this field were first examined and then the most important economic variables affecting the inflation were identified and used to predict the inflation rate. For this purpose, the seasonal data of the variables during the period of 1990-2022 were used. In this research, the methods of ordinary least squares (OLS), auto regression moving average (ARMA), auto regression with distributed lag (ARDL), Bayesian dynamic averaging (DMA) and Lasso regression were used to predict the inflation and evaluate the prediction accuracy. Therefore, based on the results obtained in this research, attention should be paid to the behavioral economic parameters of households when choosing the optimal policy. Factors such as the increase in household food prices, the high increase in workers' wages, the increase in money supply, the increase in interest rates and the increase in residential rental rates have definitely caused the formation of inflationary expectations in the society and can cause instability in the future and make the inflation deviate from equilibrium. Thus the measures to take include controlling the household food market, controlling the housing market, reforming the wage pattern in the country, controlling the interest rates in banks, and using contractionary monetary policies. These help to control and reduce inflationary expectations among the people.

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 study identifies and estimates the effective factors on housing prices over the years 1996 to 2017 in terms of model uncertainty and with BACE approach. The statistical data of 18 variables including 15 external variables (socioeconomic variables) and 3 internal variables (housing sector variables) affecting housing price according to the theoretical foundations and experimental studies have been used for this research. The results suggest that the growth of urban population, household income, the unemployment rate, the average cost of 1-square-meter building, expected inflation, income inequality, oil revenues growth, liquidity, and exchange rate are the most effective variables in housing price pattern in Iran. There is no strong evidence of effectiveness of other variables on housing price over the period of this research. The results can be used for creating appropriate patterns for explaining the issues related to housing price and better management of housing sector policies.

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.