پژوهشنامه اقتصادی
پیاپی 46 (پاییز 1391)

In recent decades, the policies of financial and trade liberalization have been considered in most of the world countries, and there are different experiences in this regard. Therefore, this study will review the effects of financial and trade liberalization on the production volatility in Iranian economy (with and without oil sector) by using Autoregressive Distributed Lag (ARDL) model for the period of 1960 to 2007.The results show that in the model that includes oil sector, trade liberalization has positive and significant effect, but financial liberalization has negative and non-significant effect on production volatility. In the model without oil, financial and trade liberalization policies both have a positive and significant effect on production volatility. In addition, the long-term relationships between the variables are approved. The value of error correction coefficient is estimated to be -0.49, which shows the amount of adjustment toward equilibrium values in the long-run. The structural stability tests for the model's strength are acceptable for the period of study.

Studying and quantifying the relationship between risk and return and identifying factors affecting the return have always been in the interest of researchers in the field of finance. Researches have shown that multi-factor models have higher power in explaining stock returns when compared with single factor models. Fama and French (1993) presented a three-factor model including market portfolio, size and the ratio of book value to market value to describe market return. The aim of this study is to add a new variable, assets growth, to this model and make a four-factor model to have a better analysis and to make a better prediction of stock market return in Tehran Stock Exchange. To accomplish this purpose, the impact of assets growth on stock return is considered under two different models which in one of them, it is not controlled for the effects of the two variables of size and the ratio of book value to market value, and in the other one, these variables are included in our model. The data are examined over a 10-year period (2000-2010) using Eviews software, and the results show that although assets growth independently does not have any significant impact on stock return, when it is added to a three-factor model that was introduced by Fama and French, it will have a negative impact on stock market return.

Energy besides Other factors production is considered the main factor in the growth and economic development and in the performance of different sectors economic can play beneficial roles. Hence, the country authorities should try to predict anything more precise energy consumption in the proper planning and guidance consumption, to control the way they desired energy demand and supply parameters. The aim of this paper reviews Comparison of Neuro-Fuzzy, Artificial Neural Network and Multivariate Regression for Prediction energy consumption in the country. Case study is energy consumption in transportation sector of Iran. So for this review, were used the annual data energy consumption of transport as a variable output of forecast models and data from the entire country's annual population, GDP and the number of vehicle as the input variables. The end results were assessed with of different models (RSE), (ME) and (RMSE). Models evaluation results showed that Neuro-Fuzzy (ANFIS), compared to other models with the highest accuracy is in predicting energy consumption.

Stock exchange is an organized and major institution in the stock market. Evaluating the performance of companies in stock exchange and selecting type of trade entity are important subjects for finance mangers and investors; because by a comprehensive assessment, investment risk could be decreased to an acceptable level. This paper aims to evaluate and rank joint stock companies by data envelopment analysis in the uncertain environment. 50 companies listed by stock exchange organization under title “50 more active companies of Tehran stock exchange-first quarter of 2010” are studied by 10 effective measures on performance evaluation. Results show that fuzzy data envelopment analysis method is an appropriate approach for modeling of fluctuations and uncertainties in different fiscal years’ data and assessment of performance of companies in uncertain states of measures.

Since many years ago, Iran has faced a macroeconomic problem of inflation. The problem of high inflation rate caused the economic growth of this country to slow down. As we all know, inflation is one of the major economic challenges that most of the countries in the world are facing, especially those in Asia including Iran. Therefore, forecasting the variable of inflation rate in Iranian economy becomes very important for the government to design effective economic strategies or monetary policies to combat any unexpected high inflation in this country. This paper studies a model of seasonal autoregressive integrated moving average to forecast inflation rates in the city of Tehran. Using monthly inflation data from March 2002 to February 2010 in Tehran, we find that ARIMA models can explain the behavior of actual data of inflation rate in Tehran in an acceptable way. Based on the selected model, we forecast the value of monthly inflation rate for six (6) months ahead in the city of Tehran that are out of the sample period (i.e. from March 2010 to August 2010). The observed inflation rate from March 2010 to August 2010 which was published by Tehran Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point inflation rate in Tehran August 2010.

The main objective of this paper is to investigate the asymmetric effects of the parity rate shocks in U.S. Dollar and Euro against Iranian Rial on the output and price in Iran during 2000:3-2007:3 period. For this purpose, the fluctuations of U.S. Dollar and Euro value against Iranian Rial have been defined as the difference between actual real exchange rate and fitted values of real exchange rate estimated by using a Markov Switching approach. Using Johansen’s co-integration test, the existence of long-run relationship between the variables of the model has been examined and both of the Lambda Max and Lambda Trace statistics confirmed that there is at least one co-integration vector between variables of the two main models of this study. Finally, asymmetric effects of U.S. Dollar and Euro shocks during positive and negative shocks on output and price level has been tested using LR test. The results show that positive and negative shocks of U.S. Dollar have an asymmetric effect on output and price level. The results also show that the fluctuations in the value of Euro against Iranian Rial have an asymmetric effect on price level and a symmetric effect on output.

Given the importance of economic effect of governmental expenditure at the national level, several studies has been done to identify the factors affecting the extent and composition of government expenditure.In the present article, the effect of fiscal corruption on government expenditure along with other factors has been studied. For this purpose, a panel which includes the annual data for years 2000- 2007 for 31 selected developing countries has been considered. In the first stage, stationary properties of variables and long-term relation between them is investigated. Then by using the method of panel data regression, the impact of fiscal corruption on the composition of government expenditure has been investigated.Findings of our econometric models show that fiscal corruption has a meaningful effect on composition of government expenditure. In other words, reduction in fiscal corruption index will lead to increase in the relative share of current expenditure, the share of expenditure on human capital and the share of expenditure on education and health in GDP. Also we have found a reduction in the relative share of military expenditure and capital expenditure in GDP.

Stock market crashes are important, both for investors and academics. Since Catastrophe Theory is a strong tool for explaining nonlinear phenomena, in this paper we apply Catastrophe theory and fit stochastic Cusp Catastrophe model to Tehran Stock Exchange (TSE) data. With the help of annual growth rate of liquidity and stock trading volume as control variables, we show that the Cusp catastrophe model explains the crashes of the TSE index in 2004 and 2008, much better than non-linear logistic model. Our results are confirmed after removing trend from Tehran Stock Exchange price index.

Terms of trade is one of the important and effective variables in the economy of all countries, and especially in the developing countries. Terms of trade shows the purchasing power of exports of a country. Therefore, the study of terms of trade and its volatility is very important in the economy of a country. In this study, by using the method of Autoregressive Distributed Lag (ARDL) and the data for years of 1975-2009, the effect of terms of trade, its volatility and components on the economic growth of Iran has been estimated. The volatility of terms of trade has been estimated using the method of Autoregressive Conditional Heteroscedasticity. The results of this study show that the effect of terms of trade on economic growth in Iran is positive and significant, but its volatility has negative and significant effect. Also, the results show that export real price index has positive and significant effect, but import real price index and export real price index volatility have negative and significant effect on the economic growth in Iran.