Iranian Journal of Economic Research

Abstract This paper investigates the effects of income per capita, financial development and openness on energy intensity for MENA selected countries for the period of 1980 - 2011 applying the panel smooth transition regression model. The linearity test results indicate strongly nonlinear relationship among variables under consideration. The optimum nonlinear model includes one transition function and one threshold parameter that represents a two-regime model. The results from the PSTR model indicated that the slope parameter of the transition function is equal to 19.99 and the location of regime switching is 9254.8 dollars. Also, the estimated coefficients of the variables in both regimes indicated that per capita income leads to an increase in the energy intensity. Openness in the second regime leads to a decrease, and financial development leads to a rise in energy intensity. 

Abstract Banks, on the one hand are involved with the challenge of inadequate cash to meet the customers’ needs and on the other hand, are reluctant to increase the costs resulting from the cash excess transfer. As a result, estimating the cash requirements of the bank's branches, according to their daily operations, which is considered as a multivariable system, is one of the most important issues in banking. In this regard, employing data mining, especially clustering methods and neural networks can help to increase the accuracy of estimating the cash required in branches. In this regard, Neural networks are considered significant in terms of flexibility, nonlinearity, greater tolerance to noise and independence from the basic assumptions about the input data. In the present paper, 20 branches of Tejarat bank have been categorized in similar clusters, during the period 21/04/2014 and 22/09/2014, according to factors such as branch grade, the type of branches in terms of deposit or facility, the number of ATMs, stand-by branches. Then, considering the clustering results and the variables related to the cash of branches such as week days, payment/ deposit subsidy/ deposit interest days, holidays and official events, as well as the amount of cash used in ATMs, the suitable structure for the neural network has been identified to estimate the required cash via the error criteria and the required cash is accordingly estimated for different clusters. The results show that the neural network, considering the clustering results, can estimate the required cash of branches in different clusters with good performance with a mean absolute error of 5%.

Abstract Inflation rate is one of the key macroeconomic variables that policymaking institutions and central banks in particular, need to forecast accurately for several periods ahead in order to make proper policies. Direct and iterated methods are two common techniques which are suggested in the literature for multi-period forecasting. In this paper, using a wide range of quarterly economic variables we compare the performance of these two techniques in real time forecasting of inflation in Iran. The results show that as the forecast horizon increases, iterated method outperforms direct method. For the information criteria which select shorter lags (e.g. Schwarz criterion), direct method and iterated method performs better in short forecast horizons (1 and 2 periods ahead) and long forecast horizons (3 and 4 periods ahead), respectively, while for the information criteria which select longer lags (e.g. Akaike criterion), iterated method generally performs better, irrespective of the forecast horizon.

Abstract Exchange rate is one of the most important factors in open economies. So determining the factors which affect exchange rate behaviors is necessary. In this research we try to analyze the role of extrapolative expectations and chartists in the instability of exchange rate market. We use unofficial nominal exchange rate data (Rial/Dollar) in weekly, monthly and quarterly horizons (from 1991 to 2015). We use fundamentalists- chartists approach and agent-based model (ABM) for simulation. The results show that when there is instability in market, the weight of chartists is much more than fundamentalists and vice versa. Also we show that chartists gain from this market so they don’t like to leave the market.  

Abstract In a risky situation probabilities of states are available.Until recently, normal distribution has been used widely in financial applications for a risky situation. Recent studies have shown that normal distribution is not appropriate for financial data and that simple variance of data as an index of riskiness is a misleading indicator of riskiness. Aumann-Serrano (2008) introduce a new economic index of riskiness to overcome these problems. In this research we use Aumann-Serrano Index to build an optimal portfolio for 23 major stocks in Tehran Stock Exchange. We compare our results with equally weighted portfolio and sharpe-ratio based portfolio and find that economic index of riskiness outperforms others with a 50.6 percent return.

Abstract Tax capacities and the lack of justice in taxing Iranian provinces are among the authorities’ concerns. Therefore, identifying the tax capacity of provinces is an inevitable necessity. Various methods of econometrics, Input-Output models, the frontier-function model, and fuzzy time series have been used to estimate tax capacity. The main problem with these methods is that they implement the estimation based on the past data. In this study, the application of fuzzy logic control (FLC) method to determine the tax capacity of the Iranian provinces in the year 2011 is one step in solving this problem. The results of the study showed that except for Tehran, there is a potential tax capacity in other provinces of the country. In most of the provinces, tax effort is not only at low level but it also has a high dispersion, which shows that taxation from the provinces has not been based on justice. Also, ability of tax payment of the country can increase.
 

Abstract The main objective of this study is to evaluate the effectiveness of the most important factors affecting the health expenditures as an indicator of health in the provinces of Iran by using a panel data model. The results of hypothesis testing using fixed effects and GMM for the period 1390-1380 show that there is a positive and significant relationship among health expenditure (as an indicator for health ) and the number of students of university, higher education (as an indicator for human capital), per capita income, and the number of main insured covered by the social security organizations on the basis of voluntary insurance.