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This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo Optimization Algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. • The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. • The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies.

Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted.• The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan.• The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process.

Granular residual is created in the banking network when there are a few large banks with many small banks. In this case, the effect of the shocks on each bank not lost in total that can lead to macro-economic consequences. The transmission of monetary policy through change in bank facilities is one of the key mechanisms affecting monetary policies. The power of transmission of monetary policy mechanism is highly dependent on the specific characteristics of the banking network. Thus, the importance of granular residual and considering it on transmission of monetary policy in the banking network is important. The purpose of the present study is the effect of granular residual in Iran's banking network on the transmission of monetary policy through lending channel. Indeed, the granular residual was imported in this study as a independent variable that can effect on bank facilities and lending channel. Therefore, in the present study, the balance sheet data of 32 banks from the banking network for the period 2001-2014 were used.Themodel used in this study was estimated through the Generalized Method of Moments (GMM). The results showed that in the first, according to available theories in relation to the granular residual and conducting relevant empirical analyses, the presence of granular residual in Iran's banking network is confirmed. Also as expected, granular residual has a positive and significant effect on Iran's banking network facilities and the transmission of monetary policy through lending channel is weakened by increasing the granular impacts.

This study examined the effects of the real exchange rate and inflation uncertainty on the investment function of Tobin’s Q, in the period 2001-2010. To proceed, the data of 100 listed companies in the Tehran Stock Exchange were collected. The theoretical model were estimated by introducing the real exchange rate, and then, the inflation variable in the model. Exchange rate uncertainty was estimated by the generalized autoregressive conditional heteroscedasticity (GARCH) model, and the interactive correlations of the model variables were investigated by the GMM method. The results showed that the effects of the real exchange rate and inflation uncertainty variables in Tobin’s Q investment function are negative and significant. Also, the first lag of investment, cash flow, Tobin's Q and its lag variables have positive and significant effects on investment, and that the lagged cash flow, and the lagged real exchange rate uncertainty and the lagged inflation have negative and significant effects on investment.

In this paper, first, financial markets have been analyzed and then financial institutions in economy, in addition to industrial and mineral investments in developed and developing countries, specially Iran, have been compared. Using estimation method 3SLS, the selected model has been estimated. The model consists of a trilogy, the variable functions of which are private investment in the industry and mine sector, public investment in industry and mine sector and the amount of liquidity. Balance of bank credits to the none governmental sector, risk index, bank's credit rates, real exchange rate, inflation rate, domestic national product and governments indebtedness to commercial and specialized banks being the independent variables. According to the results of the research, the bank's interest rate creates no sensitivity in the investment of the private sector in industry and mine in Iran; but the balance of the banking credit to the none governmental sector in industry and mining sector has had a positive effect on the private investment in industry and mine.

The purpose of this paper is to answer the following question: Is estimate the price and income elasticites of the demand for health services in Iran elastic?. This researchWe use has used the Almost Ideal Demand System (AIDS) model. AIDS model using the household expenditure data for the three subsections of medicine, user fees for doctors' and fees and hospitals ization services for three income groups for thein the period 1971 – 2001. One conclusion isOur results indicate that, the health services should be considered as aare necessity for all income groups. In this case, cross elasticities showed and that hygiene and/or mMedical services have complimentary relations with cloths and shelter. Price elasticitiesy of hospitalization for low, medium, and high income groups was are low: -0.52, -0.53 and -0.62, respectively. These results indicate of low elasticity of hospitalization services for all income groups and this issue Overall, we conclude that the confirms that elasticity for health services is low in Iran.