Journal of Quantitative Economics
Year:2013 | Volume:10 | Issue:3 (38)|Papers Count:6

Energy has had a determinative role in economic growth of countries and its importance has been increasing during recent decades. The global economic growth and the industrialization process is one of the main sources of increasing demand and consumption of energy. On the other hand, the residential and commercial sectors are the largest energy consumption sectors, i.e. 34% of energy consumption compared to other sectors. Thus, to plan and control the amount of energy supply and demand, predicting energy consumption of these sectors would be helpful for policy makers. In this paper, the future status of energy demand of residential and commercial sectors in Iran is predicted using variables affecting energy demand of these sectors. Using PSO algorithm, both linear and exponential forms of energy demand equations were studied under 54 different scenarios with various variables. The data from 1968 to 2011 were applied to develop model and to choose the appropriate scenario. Results show that an exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most appropriate model. Furthermore, energy demand of residential and commercial sectors is estimated for following years up to 2032.

This study provides a dynamic estimation of Capital Asset Pricing Model in optimal dynamic household decisions during its life cycle based on Dynamic Conditional Correlation (DCC) and State- Space Models. In this regard, for daily time series data of Vehicle and parts manufacturing stock price for the period of 2006 to the end of sixth month of 2011, dynamic estimation of Beta-index time series which obtained from DCC and State-Space models has been compared with static linear regression model. The results show that the DCC model has minimum error in out-of-sample forecasting of stock returns based on Root Mean Square Error (RMSE) criterion. However, state-space model has minimum error for in-sample forecasting based on RMSE criterion.

Exchange Rate uncertainty means an unpredictable change in the exchange rate that can cause a great impact on other economic variables and institutions such as banks and credit institutions especially in developing countries. In this study, using monthly data from the 2009/3-2013/9, the effect of recent exchange rate crisis on Iranian banks and financial institutions is estimated and predicted. To do that, models such as VAR, VECM, Genetic and PSO Algorithm are employed. The results of estimating the VAR model indicates that there is a negative relationship between exchange rate volatility and stock market returns. Further, Cointegration test results imply the existence of an indirect long-run relationship between these two variables. By simulation of linearly and exponentially models using Genetic and PSO algorithms to compare the accuracy of these models with estimated VAR model, it was determined that VAR model can predict more accurately. Prediction results showed that persistence of exchange rate fluctuation can lead to first a diminishing increase followed by reduction in the index. However, reduction of exchange rate can lead to an increase in financial and credit institution's stock index.

Fiscal illusion refers to a systematic misperception of fiscal parameters and an associated pattern of over- and underestimation of expenditure and taxation liabilities which makes biases in budgetary decisions at all levels of government. This paper provides the empirical analysis of the estimates of fiscal illusion index for Iranian economy over the period of 1981 to 2012 employing a structural equation model approach in framework of Multiple Indicators-Multiple Causes (MIMIC). To this end, we have used tax burden and level of education as cause variables and public debt, the ratio of direct tax to indirect tax and the ratio of private consumption to public debt as indicators of fiscal illusion. The results show that tax burden with a coefficient of 0.79 is the most important factor for explanation of fiscal illusion. Policy makers attempt to ‘conceal’ the real tax burden by means of delusion of public debt and of private spending to the levels of public debt. Further, size of fiscal illusion has upward trend that indicates policy makers usually exploit fiscal illusion stratagems to distort taxpayers’ perceptions of their tax burdens.

This study investigated the effectiveness of monetary and fiscal policies’ targets, determined in the 4th national economic development program of Iran, using dynamic simulation approach. To this end, a macroeconometrics model was designed for Iran economy. In model specification, it was tried to consider the influential mechanisms of monetary and fiscal policies on all important sectors of economy. Equations were estimated for time period of 1971-2004 using Auto Regressive Distributed Lag method. When the models’ accuracy was confirmed, the impacts of monetary and fiscal policies’ targets, determined in the 4th program, on some important macroeconomics variables were predicted for time period of 2005-2013 using dynamic simulation. According to results, even though the application of the program’s targets reduces economic fluctuations, it cannot, however, put the economy on the determined mid-term and long-term path. Therefore, investigation of quantitative targets of monetary and fiscal policies in accordance with structural feature of Iran economy is recommended. However, in addition to the determination of mid-term and long-term targets, it is necessary to develop short-term compensating policies, especially for monetary policies, in order to minimize the deviation of the realized values from target values.

One of the essential components of economic development and welfare is the security. Security is a source of psychological welfare. Moreover, security lives and property, the welfare of illusion and failure to attract investment and promote instrument market. Therefore the purpose of this study was to estimate the index of crime in Iran using the fuzzy approach during the period. variables of poverty and unemployment rate using as causal factor so of crime in order to estimate the index of crime. For estimates the poverty index, Gini coefficient, inflation and per capita income are used as input variable. The results of this study show that the index of crime have been fluctuating in some years but in recently years the trend index of Crime has been upward and follows the poverty index significantly. Crime index shows the lowest crime took place between 1978 and 1980, then increased until 1997, but has faced with slight fluctuation. It has rising again since 1997 and reached the highest level in 2011. In the years 2009 to 2011, because of the recession and sharp decline in economic growth, poverty index and crime index have faced with highest level.