فصلنامه پژوهش ها و سیاست های اقتصادی
پیاپی 59 (پاییز 1390)

The oil-export revenues can impact the components of monetary aggregates; through the Iranian government budget; resulting in the loose of the Iranian central bank's control over the money and inflation. The empirical evidences show that the positive and the negative oil-export revenue shocks increase the inflation rate in Iran. This paper intends to build a Neoclassical Dynamic Stochastic Model to analyze the channels through which the oil-export revenues impact the Iranian economy and inflation rate; on the basis of the assumption that the monetary, fiscal and development policy measures are highly inter-related.

Technology advancement and investment in research and development (R&D) activities are the most important determinants of economic growth. According to the new theories of international trade and endogenous economic growth, investment in foreign research and development activities causes transfer of technology and new know-how to the country. Human capital also has a major role in expanding and deepening of domestic R&D activities and absorption of international R&D. Because, the country's ability to absorb foreign know how, is, inter a lid, highly dependent on human capital. The purpose of this paper is to study the effect of domestic technology and technology spillovers from the two channels of intermediate and capital goods imports as well as FDI, on the economic growth of Iran, during (1954-2008). The results indicate that accumulation of domestic R&D, and stockpile of foreign R&D from the channels of intermediate and capital goods imports and FDI and also labor force and accumulation of physical assets, have all positive and significant effects on the economic growth. In addition, in this paper we discussed various economic shocks and variance decomposition effects on the economic growth and also the impact of their changes on the rate of GDP growth. In the long-run, accumulation of capital and domestic R&D، labor, stockpile of the foreign R&D from the channels of imports and FDI, explain the most percentages of variance of error-predicting, respectively.

This paper examines the role of insurance industry in the economic growth of Iran, by using a hypothetical extraction method (HEM). To estimate the causality relationship(Toda and Yamoto Test), the time series analysis for (1967-2007) period Input-Output tables of 1986 and 2001 are used. The results show the existence of a one way relationship from insurance to economic growth, but only for non-life insurance. However, no relationship is observed for life insurance. This finding indicates the insignificant role of insurance sector in Iran’s economic growth.

In this article, using New- Keynesian approach for inflation and unemployment which focuses on the menu costs and rigidity of prices and wages, first a bi-variate system including inflation and unemployment and a tri-variate system with inflation, unemployment and mark-up of the costs of labor force have been estimated. Then the long-term relationship between these variables has been estimated by using co-integration and VAR models. The estimation of these two systems gives a positive and long-term relationship between inflation and unemployment. Also the relationship between inflation and mark-up of the cost of labor force has been estimated which is negative and therefore, there is a positive relation between inflation and unemployment. Such Positive relationship indicates chronic stagnation phenomena in the Iranian Economy.

This study tries to measure core inflation in Iran's economy, using SVAR method, during the period (1973-2007). Importance of understanding core inflation lies on the fact that the core inflation reveals the likely noises (shocks). By using core inflation criterion in policy making, monetary policies become more effective, as policymakers just react to the fluctuations in measured inflation, ignoring temporary noises. In an attempt to measure core inflation in Iran, three variables, namely oil price, gross national product, and consumer price index are deployed in a Structural Vector Auto Regressive (SVAR) model, with imposing some restrictions to make the model consistent with the structure of Iran's economy. The results show that in most cases, headline inflationary pressures have been more than measured inflation, owning to the deflationary pressures of oil export revenues in the economy.

In this research, the effect of banking facilities on the value-added of the industry and mining sector of Iran has been studied. By using time series data for the (1977-2000) years and estimating simultaneous equations model by employing 3SLS method, we have shown that, on average, the production elasticity's to the banking facilities for investment as well as for working capital are 0.05 and 0.14 respectively.

Nowadays, tourism industry is one of the most important economical sector in many countries and development of this industry receives priority by many of the governments. Given its natural and comparative advantages, ancient civilization and religious and historic places, Iran is a suitable region for investment in tourism industry. One of the most important factors conducive to the development of this industry is banking credits and loans. This study trying to make a Fuzzy Multiple Attribute Decision Making Model (FMADM), for determination of the economical investment index, priorities and optimum percentages of credits allocated for investment in tourism industry of Iran's provinces. for this purpose, we take a combination of 15 economical indexes and Fuzzy Logic, to account uncertainty conditions and fluctuations in inputs and outputs quantities. This study covers all the Iranian provinces, for the period of (2001-2008). The results show that, the provinces of Tehran, Hormozgan, Mazandaran, Khorasan Razavi, Gilan, Esfahan, Golestan, Ghom, Fars and Yazd maintain the rankings of one to ten, in terms of priorities for investment in tourism industry respectively. Furthermore, given the uncertain conditions and fluctuations in inputs and outputs in this industry, current structure of banking credits and loans, extended to tourism sector, is not optimum and there is the need for adjustments in volumes of banking loans and percentages of credits, allocated to this sector.

This paper examines the short-run and long-run relationships between electricity consumption and economic growth in Iran in the supply side model, in the period (1972- 2007)also the influence of price liberalization on economic growth and electricity consumption in the demand side model are estimated for the country in the same period. In order to investigate these relationships ARDL bounds test approach is used. The results of supply model suggest a negative unidirectional relationship running from economic growth to electricity consumption in the long-run. We also find a positive and bidirectional relationship between economic growth and electricity consumption in the short-run. The results of demand side model indicate that there are no long-run relationships among electricity price with electricity consumption and economic growth. Therefore electricity consumption in Iran does not necessarily lead to economic growth and liberalization policies of electricity price does not adversely affect on economic growth.

The aim of this study is to introduce appropriate models for forecasting the gas oil prices in Singapore market which influence the gas-oil price in the Middle East. The used data are on weekly basis and covering the period of (1997-2010).The forecasts were made for 10, 20 and 30 percent ages of the data, separately. The models have been employed for prosecutions included four models of Neural Network and ARIMA model. The 4 selected Neural Networks are Feed-Forward Back Propagation, Cascade Back Propagation, Elman Back Propagation and Generalized Regression. The training functions are also Levenberg- Marquardt and Quasi-Newton BFG. The results that the least forecasting error belongs to the network in the Levenberg-Marquardt training function has been used. The results also reveal that for forecasts of 20 and 30 percent of data, Elman Back Propagation and for the 10 percent of the data Feed-Forward Back Propagation networks have the least forecast error. Moreover, the findings also reveal that Generalized Regression network and ARIMA have the largest forecasting error as compared to the other models. However, the findings of Diebold-Mariano statistics showed no statistically significant difference among the networks with the least forecast error, in respect to forecasting accuracy for the gas-oil price using a combination of 80 percent for training and 20 percent of day a for forecasting, as compared to the data combination, how less forecasting error. Finally, forecasting error of in the case of the best model is around 2 percent.

Assuming that in long-run, money is the main determinant of price level, we can state that long-run is a period in which money and prices are highly correlated. Given this assumption, we have calculated the length of the long-run periods of different economies by using Christiano-Fitzherald (2003) Filter. The results show that we cannot consider the same period as long-run for different economies. We have calculated the length of this period for different economies. These periods are longer for economies facing more supply shocks.