Iranian Journal of Economic Research
Year:2007 | Volume:- | Issue:32|Papers Count:9

Freedom of choice and competition are viewed as vital factors for economic growth. In this paper, we use a growth model to examine the impact of economic freedom on the economic growth across the world countries for the period 1999-2004. Our study differs from the previous studies in terms of the modeling, the scope of the study, and the selected sample. The results, however, are consistent with those of the previous studies. Given the fact that Iran's position in the world is low in terms of economic freedom, that economic freedom has a significant role in economic growth, it is necessary for investors and firms to have suitable conditions for transparent and accurate decision-making. Creation of the favorable economic conditions is the responsibility of the government and thus, it is necessary to have defined policies and planning for the establishment of conditions for economic freedom in the society. Creation of social condition for economic freedom of economic agents is a convenient orientation that is favorable with economic growth policies and must be internalized in the Iran's economic policies.

The purpose of this paper is to investigate the effect of some macroeconomic variables on house price index. We apply a macroeconomic model with micro foundations that uses household income, stock price index, building service price index, housing completions, supply of money, and inflation to explain the changes in house price index. We use the ARDL and the ECM model with the seasonal data for the period 1995-2006 to estimate the speed of convergence to equilibrium. Our findings indicate that the macroeconomic variables can explain the changes in the house price index in Iran, and the sign of estimated coefficients confirm the hypothesis.

Anti poverty program in Iran has started in 2001 to mitigate poverty. This paper addresses issues related to the dynamics of poverty using household panel data for urban and rural areas of Iran covering the period 2001-2003.For this purpose poor and non poor households have been identified by estimating rural and urban poverty lines. The persistence of poverty during this period has been investigated applying spell approach. Also, determinants of reentering and exiting poverty rate have been studied using the logit model. Food Energy Intake (FEI) method and nonparametric regression have been used to estimate food and non food poverty lines. Results indicate that poverty is more widespread in rural areas than in urban areas and is more persistent in urban areas than rural areas. Also study shows that chronic poverty is more common in urban areas.

Traditional methods of deciding whether to grant credit to a particular individual use human judgment of the risk of default based on experience of previous decisions. However, economic pressures resulting from increased. demand for credit, allied' with greater commercial competition and the emergence of new computer technology have led to development of sophisticated statistical models to aid the credit granting decision making process. Credit scoring is the name used to describe this process of determining how likely applicants are to default with their repayments. Credit scoring has some obvious benefits that have led to its increasing use in loan evaluation. For example, it is quicker, cheaper and more objective than judgmental method. A wide range of statistical methods such as discriminant analysis, logistic regression, and neural networks have been applied for credit scoring. In this paper, we design a neural network credit scoring system for classifying the applicants of personal loans in bank and compare the performance of this model with discriminate analysis and logistic regression models. The results of this investigation show that the neural network model is more accurate and more flexible than discriminate analysis and logistic regression.

Granger and Newbold (1974) proposed the idea of spurious regression in econometrics. They showed that with 1(1) dependent and independent variables, if a regression model is estimated by OLS method, the results may be spurious. This idea is extended to variables with different order of integration. In this paper, we review the literature of spurious regression and show that when the variables have different order of integration, for example I(1) & 1(2), and I( 1)& 1(0), the spurious results may occur.

The residential and commercial sectors are the main consumers of natural gas in Iran. The demand for natural gas in these sectors is on its peak in the cold seasons for the heating. In addition to changes in temperature, which is the main determinant of demand fluctuation in energy, other factors such as unobservable seasonal shocks affect the seasonal demand. Moreover, observable economic factors such as price and income, as well as non economic factors, like changes in consumer's taste and technical progresses, affect the energy demand. In this paper, we use an applied structural time series model (STSM) approach, which considers both stochastic trend and stochastic seasonality, to estimate the price and income elasticities for the natural gas demand in Iran. We apply the kalman filter with a maximum likelihood estimation method to provide unbiased estimators for the parameters. According to our results, although the estimated demand for natural gas does not have a trend component, the nature of seasonal component is stochastic. The elasticity of demand with respect to temperature is -0.26 percent and the long-run income and price elasticities are 0.17, -0.13, respectively.

Ability of Artificial Neural Networks (ANNs) as a powerful tool in simulation and prediction in science and engineering has made it attractive to economists. In this article, after a brief review of literature, a comparison of forecasting performance of ANN versus ARIMA is made. The data used are daily prices of oil for the period April 1983 to June 2005. In addition, sensitivity analysis is implemented for illustrating contribution of each input to the price changes in ANN models. The results show that the ANN model generates more accurate forecasts for the daily oil prices of oil than ARIMA model.