Journal of Quantitative Economics
Year:2011 | Volume:7 | Issue:4 (27)|Papers Count:7

In this study, the minimum salary of urban households in Kermanshah province as a subjective and relative measures of poverty, with many poverty indicators over the period 1386-1376 using a dynamic linear expenditure system, assuming the formation of habits (HLES) and regression of seemingly unrelated Iterative (ISUR) we have estimated. Data used in this study includes data budget (revenue-cost) of urban households in Kermanshah and relevant price indices and estimation using six commodity groups has been done. Research findings show the total minimum annual salary during the process of Kermanshah for urban areas is increasing. Also, the urban poverty line adjusted price index increasing trend of your shows eh. According to the calculated minimum wage, compared census indexes, and income inequality than the income gap between the poor (FGT) and also for urban areas Kakwani calculated results indicate that the overall deterioration of the situation of urban households households during the investigation.

This paper analyzes both economy of scale and scope in Iran's banking system, using unbalanced panel data models for 17 banks functioning in Iran's economy, spanning 1996-2009. To study economy of scale and scope in banking, various measures of economy of scale and scope were examined. Overall scale economies shows that the iran banking system have scale economies. But within-sample product-specific scale economies index results shows that al mostly there are economies of scale in all of the banking activity only in private banks. Also within-sample overall scope economies is positive and shows that there is economy of scope in iran banking system. wpscope index shows that there are economies of scope in loan, deposite and off balance sheet activity only in private banks and investment activity in oll banks.

The aim of this paper is twofold, one of them is to measure the permanent (core) and temporary components of inflation in the context of the Iranian economy during the period 1961–2006. The other is to estimate the impact of each component of inflation on the private investment in short-run and long-run. To do so, first we use kalman filter method to decompose the actual inflation in to permanent and temporary inflation, then an ARDL model employed to estimate the effect of permanent and temporary inflation on private investment. The results show that the temporary inflation negatively influences the investment in short run, but in the long-run it has no effect. Also these results indicat that an increasing the core inflation in long-run reduces the private investment, but in the short-run it has no effect. Therefor, the monetory and fiscal policies which are designed to adjust the inflation must be used in such manner that control the inflation but not negativly affect the economic performance.

We can define money-laundering as concealing the money acquired from the process of criminal economic activity that its root may be legal (like tax evasion) or its base may be illegal (such as drug-dealing or human trafficking). At the moment, law-makers are facing many challenges regarding these criminal activities all around the world. In this paper we try, as a first attempt, to estimate the amount of dirty money in the Iranian economy by using the unobservable variable approach which barrowed from Khajavi et al(2010). In this paper we removed many restricted assumption, applied by previous researches, and in this way we get new estimate of the illegal money in the economy of Iran during 1352-1386. Our results show that there is positive relationship between illegal money, on the one hand, and underground economy plus demand for money, on the other hand. We suggest that economic policy-makers should watch this relationship much more carefully.

Variance and standard deviation can be used for measuring risk of portfolios. When return distribution is normally distributed they are suitable measurements. But there are situations in which that condition dose not exist. for instance if the minimum value of stock return is -100% the minimum value of stock is zero. Therefore recently some criterions like kurtosis and skewness have been more common. in this article we use these new measurements and Fama and French 3 factor model to compare their ability to determine of difference in stock returns. Based on data from march 2001 to march 2005 of Tehran Stock Market companies our results show that market risk premium, Size of company and kurtosis are more efficient than other factors like B/M and skewness in determining the risk of portfolios. consequently it is suitable for investors to use them for performance measurement and portfolio optimization.

The availability of data for the size of the "Smuggling volume" is important for macroeconomic policy. We use fuzzy set theory and fuzzy logic to construct an annual time-series for the (unobservable) Iran Smuggling Index over the period 1350 to 1385. Three input variables are used-the tax burden, unemployment rate and an index of the exchange rate gap (i.e. the gap between market and interbank rates). The resulting smuggling index time-series is compared with one previously constructed by the second author using a structural "Multiple Indicators, Multiple Causes" (MIMIC) model. The two approaches each yield sensible, but somewhat different, pictures of the Iran smuggling index over this period.

There are different statistical models for prediction and classifications in science. Statistical and econometric models like regression, discriminant analysis, time series, ratings and the others are used for forecasting and classifying, based on variables and data about certain subject. However, statistical models have many presumptions and limitations, so in spite of different existing statistical models, recently, neural networks as modern method of forecasting have received considerable attention for its high efficiency and no needs to the presumption and limitation in data distributions. This study is aimed to compare the ability of artificial neural networks model to that of logistic regression and discriminant models in forecasting default events. To do this, logistic regression, discriminant analysis and neural network models were evaluated using the data of 23801 contracts and taking the contract time, contract amount, industry type, contract type, guarantee type and credit policy as forecasting variables. The ability of models in predicting default risks was compared using ROC analysis and the classification accuracy reviews. The results of this study indicate that the variables mentioned above are significant in forecasting defaults and artificial neural networks model is more efficient as compared to the two other models.