Journal of New Economy and Trade
Year:2019 | Volume:13 | Issue:4|Papers Count:7

Foreign direct investment (FDI) and increased trade volume have been known as resources for capital accumulation and economic growth in developing countries. FDI and trade are also associated with technology transfer, linkages with international markets, and transfer of management skills. However, increasing economic growth requires higher energy consumption, which leads to environmental degradation in these countries. This study probes the effect of foreign direct investment and trade openness on Carbon dioxide emissions (as an indicator of environmental quality) and clean energies in the selected developing countries using a panel-ARDL approach during 1971-2014. The findings of this study reveal that despite the positive and significant effects of direct foreign investment on economic growth and the use of clean energy, they have no significant effect on the environmental degradation in these countries. The results also show that trade openness has a positive and significant correlation with carbon dioxide emissions, while this variable has a negative and significant effect on clean energy consumption. Finally, the results of the study indicate that the speed of adjustment towards long-term equilibrium in this model is relatively slow.

Renewable energies have attracted more attention due to the increase in the demand for energy, limited fossil fuel resources, and rising greenhouse gas emissions. However, high investment costs can be considered as one of the major problems for the development of renewable energy technologies. In this study, we design a model to investigate the relationship between price, biogas technology development, and the benefits of stakeholders based on the system dynamic approach. The results of various simulation scenarios show that the state variables have greater effect on the technology development price than the market variables. Moreover, the results reveal that the government subsidies are more influential in the process of the technology development and pricing, compared to the other state variables.

Exchange rate is one of the most important variables in economics, which impacts many other macroeconomic variables in an economy. Such changes are more important in developing countries, which are often exporters of raw materials. Exchange rate fluctuations in Iran is also critical since the country is highly dependent on foreign exchange earnings from oil exports and also subject to the severs sanctions imposed by the US in recent years. In this paper, we propose a forecasting model using recurrent self-organizing maps to forecast the exchange rates considering the factors affecting the exchange rate markets. In total, 12 selected variables are used in modeling the exchange rate, which includes the effective macro variables and the price index of competing markets, such as gold, stock exchange and housing. We also control for the effect of sanctions. The results show that the model produces relatively accurate forecasts of the exchange rates with a less than 3% error.

This paper investigates the effects of energy consumption, ICT and research and development spending on the manufacturing industries production in Iran. We use a panel data from 15 industries for the period 1374-1393(2005-2014). The results show that energy consumption, ICT and research and development spending have positive and significant effects on the growth of manufacturing output. In addition, energy consumption has the most impact on manufacturing industries. Estimates of the coefficients show that the elasticity of output with respect to energy consumption, ICT and R&D expenditures are 0. 516, 0. 131 and 0. 110, respectively.

We apply Almost Ideal Demand System (AIDS) to estimate internet demand by Iranian urban households. using the Seemingly Unrelated Regressions (SUR) method and pooled data of more than 130 thousands of Iranian urban households for the period 2008-2014. In order to increase robustness in estimation, we use provincial price indices and divide goods into 15 groups. The results show that the internet in Iran is a luxury (income elasticity of demand equals to 1. 94) and an elastic (price elasticity of demand equals-1. 5) service. Moreover, the results show that the internet is substitute with recreation, newspapers and other communication devices and is complement with telephone and education.

The exchange rate is one of the most important economics’ variables which could impact the foreign trade and balance of payments and other macroeconomic variables such as GDP, inflation, and employment. Given the dependency of Iranian economy on foreign exchange and its high volatility over the past thirty-five years, forecasting the exchange rates and their volatility help reduce the risk in business and government plans. In this paper, we model the Iran’ s official exchange rate using the autoregressive ARIMA intervention approach and compare its results with the random walk model. We use the official exchange rates data from 1357 to 1394 to estimate the models and forecast for the period 1395 to 1404 using R. The results show that the ARIMA model with intervention performs better than the random walk model.

In this study, we analyze the fiscal and monetary policies in Iran using an open DSGE model in which the central bank’ s policy is in the form of a McCallum rule. We estimate the structural parameters using the Bayesian method and the quarterly time series data for the period 1369 – 1394 (1990-2015). To check the validity of the results, we use the Monte Carlo Markov Chain statistics, Gelman – Brooks statistics, and also compare prior and posterior distribution functions. The results indicate a good fit of the model with the data. The simulation results also show that fiscal and monetary policies have real impacts on variables in the short run.