In this paper, a model based on GMDH Type Neural Network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. The results suggest that GMDH Neural Network model, according to the Root Mean Squared Error (RMSE) and Direction statistics (Dstat) statistics are more effective than OLS method. Also, first lag of gas price in the future market is the most efficient variable in predicting gas price in spot market.
This paper investigates the key factors affecting the foreign direct investment (FDI) inflow to developing countries during the period (1995-2010) with emphasis on the financial development. Financial development, as an important factor in FDI absorption and a prerequisite for utilizing the benefits of FDI, not only increases the FDI inflow in developing countries, but also improve the absorption capacity and ability of these countries to utilize the benefits of FDI. Since the financial system consists of several components and provides a variety of services, various indicators, which represent the development of different aspects and components of financial system, have been applied in order to assess the impact of financial development on the FDI. Results indicate that development of various components of financial system (stock market and banking sector) as well as different aspects of financial development (size and activity level of financial system) all have positive and significant impact on the FDI inflow in developing countries during the studied period.
This paper investigates the short- run and long-run effects of government size and exports on the economic growth of Iran as a developing oil export based economy for the period of 1974 - 2008 using an autoregressive distributed lags (ARDL) framework. A modified form of Feder (1982) and subsequently Ram’s (1986) model has been applied to include both government size and exports in growth equation. The findings show that in long run and short run the Armey curve (1995) is valid, indicating that both a very big size and a too small size of government are harmful for growth and government should adjust its size. The results also show that total exports, the amount of oil exports in terms of barrels and oil prices affect economic growth positively and significantly both in short-run and long-run. However, non-oil exports do not have a significant effect on growth in the long run
The main objective of this paper is to evaluate the effect of R&D on profitability of high -tech industries with new evidence from the Iranian industries carrying four- digit codes. The Structure- Conduct- Performance (SCP) paradigm, which is relatively well known in industrial economics and in organization management, provides the theoretical construct that guides our empirical model formulation and execution. The data is compiled from observations made at the plant level covering all industrial plants employing ten or more persons and carrying four- digit codes within the time span of 1994-2007. The model used essentially consists of a simultaneous equation system framework grounded into a panel data approach and estimated by Two-Stage Least Squares (2SLS). Our findings indicate a positive and significant effect of R&D expenditures, measured in intensive form, on profitability of high-tech industries as evidenced by the Iranian case. Our more notable finding is the positive effect of lagged profits on R&D expenditure intensity, revealing a likely mutually enforcing relationship between profitability and R&D intensity in high tech industries
In this paper, we will review the foreign exchange market and will try to extract an exchange market pressure and an intervention index for Iran by following the Weymark (1995) approach to evaluate the Central Bank of Iran’s exchange rate policy during 1368:Q1 to 1391:Q3. The estimation method employed, is the econometric technique known in the literature as the Two-Stage Least Squares (2SLS).The exchange market pressure’s mean value of 0.062 provides evidence that depreciating pressure remained dominant over the entire sample period. Also, the mean value of the intervention index is 0.44, indicating that the foreign exchange reserve and exchange rate changes absorbed forty-four and fifty-six percent of the pressure, respectively. Otherwise the results of the paper show that on an average there was a downward pressure on Iran’s currency and the Central Bank of Iran pursued an active intervention policy. Specifically, as the intervention index shows, the Central Bank of Iran used both exchange rate and foreign exchange reserve interventions for restoring the foreign exchange market to equilibrium levels, a policy known as the managed float exchange rate regime.
In general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. It is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. One could argue that these random changes act like noise which effects the deterministic variations in prices. In this paper, we employ the wavelet transform as a tool for smoothing and minimizing the noise presented in crude oil prices, and then investigate the effect of wavelet smoothing on oil price forecasting while using the GMDH neural network as the forecasting model. Furthermore, the Generalized Auto-Regressive Conditional Hetroscedasticity model is used for capturing time varying variance of crude oil price. In order to evaluate the proposed hybrid model, we employ crude oil spot price of New York and Los Angles markets. Results reveal that the prediction performance improves by more than 40% when the effect of noise is minimized and variance is captured by Auto-Regressive Conditional Hetroscedasticity model.