siamak elmi

The economy of many countries around the world is now dependent on commercial activities in ports. Although ports are considered among the most important transportation infrastructures that can greatly affect economic growth, less attention is paid to ports and their relevant commercial activities in Iran. This study used the mixed-data method to address this issue by reviewing the data related to Khuzestan, Bushehr, Hormozgan, Sistan and Baluchestan, Guilan, and Mazandaran provinces of Iran in the period 2005-2017. The results showed that the operating capacity of ports exhibited a significant and positive effect on the economic production of the studied provinces. In addition, the uploading and offloading volume showed a significant but nonlinear effect on the economic production of hosts. In other words, the economic production increased with the increase in the operating capacity of ports but further increase in the operating capacity beyond the critical point negatively affected economic production. The study findings also indicated that human development, economic growth rate at the beginning of the studied period, employment, government investment in ports, government’s working budget, technology, manufacturing structures, and urbanization rate had positive and significant effects on the economic production of the studied provinces.

With having 80 percent of world trade, ports nowadays are the most important national and international window towards trade. Despite their Increasing importance in development of transportation, ports and the relevant activities are completely neglected in Iran. Significant relationship between ports development and their capacity and throughput drive an important question: what is the net effect of port throughput on the host areas growth? The present study analyses the question using data of six provinces including Khoozestan, Booshehr, Hormozgan, Gilan, Mazandaran, and Sistan and Baluchestan during 2005-2017 (1384-1396). Results show that ports capacity, human development, economic growth at the initial period, employment, government investment in ports, government's current budget, wage, technology, production structure and urbanization have significant positive impact on economic output of the provinces while the transportation cost has a negative one.

Inflation is a major problem in many developing countries and high levels of inflation leads to economic growth reduction، misallocation of resources and relative prices disorder. Since linear models have been used in most of the for time series modeling، so this paper using a nonlinear model aims to increase the empirical literature of the inflation sustainability in Iran. This study using the nonlinear STAR models examines the hypothesis that the monthly inflation rate has been stable during 1962-2007. In order to study the unit root hypothesis the Ng، Perron and KPSS testes also have been used. Results suggest that the monthly inflation rate in Iran is nonlinear and follows the LSTAR model. Results also indicate that the monthly inflation rate is locally unstable، so in low regime (less than 2/5 percent) is stable and in a high one is unstable and whenever inflation is in high regime the cost of each inflation rate control policy to is more than it’s benefit، but stability analysis based on unit root tests indicates that monthly inflation rate in the economy of Iran has long-term structure

This paper analyzes the impacts of inequality in the intra- provincial’s level on economic growth، based on steady state growth model، during (2000-2007). The results indicate that physical capital، human capital، communication networks، openness of the economy، industrial structure، population growth rate، access to the sea، geographical boundaries as well as distance from the capital city are the main influential factors on the economic growth of the provinces. It also affirms the convergence among provinces.

Chaotic theory suggests a new method for studying trends in markets and reveals the hidden pattern behind the data that common models can not define them. The usual tests for chaos are calculation of the largest Lyapunov exponent (Le) and strange attractors. A positive largest Lyapunov exponent indicates chaos. Positive amount of "Le" shows chaoticness of process and difficulty of predictability and when it is negative, it shows that process in the long run is non chaotic and predicable. If "Le" moves towards positive quantity near zero, chaotic system is weak and middle term predictability is possible. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) from 1992/3/25 to 2007/5/23 and two artificial models with regard to the Takens embedding theorem have been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom, in addition; trend in Iran Exchange Market is not linear and we can not forecast it with common linear methods.

Nonlinear dynamic systems exhibit different behaviors, that can be utilized to explain many economic phenomena that seems to be stochastic. Chaos theory suggests a new method to study the changes of nonlinear dynamic systems in the financial markets. In this paper, we studied Iran foreign exchange rate sensitivity to initial conditions by chaos theory and maximum Lyapunov exponent against the U.S. and Canadian dollar, British Pound, the Euro and the UAE Dirham during 24/03/1982 to 23/05/2007. For this purpose, we analyze the presence of chaos behavior in the above mentional currencies by strong correlation test and maximum Lyapunov exponent. The obtional result indicate that Iran foreign exchange rate against the U.S. dollar is and fellows a chaos process. Therefore the linear methods are not appropriate for prediction of the variable. In second part of this paper, we predict the Iran exchange rate against the U.S. dollar by nonlinear neural Network model using optimizing self comparative particles group algorithm. Results of neural network algorithm shows that daily exchange prices in a short period are highly predictable based on prior price.