Journal of Quantitative Economics
Year:2009 | Volume:6 | Issue:1 (20)|Papers Count:14

The necessity of taking human capital and investment into account for economic analyses is clear to a large extent according to the relevant literature. Majority of the studies in this area have exclusively concentrated on the role of education with positive effect on increasing the productivity of labor. However, labor’s experience and health constitute the main part of human capital in addition to education. This research aims at the analysis of the role of the quality of labor in connection with the effect of variables of labor’s education, experience and health on economic growth. In this research, total production function has been used on the basis of Mincer Wage Equation to study 75 countries classified on the basis of income between 1960-2000 in panel data method. In high income countries, the results show a significant and positive effect of the variables of labor’s education, experience and health (proxied by life expectancy) on economic growth. The result is the same in the middle income countries. In low income countries, only the variables of labor’s education and experience have been efficient on economic growth.

The main purpose of this paper is to study the effects of macroeconomic shocks on unemployment rate in the context of the Iranian economy. To do so, we use a structural vector autoregressive (SVAR) model along with Impulse-Response Function and Variance Decomposition Method over the period 1966-2007. The overall results indicate that productivity and aggregate demand shocks have significant negative effects on unemployment rate in Iran; however, wage, price and labour supply shocks have significant positive effects on the independent variable in this study.

In order to get future information about economic variables, economists have discovered new methods. Large reserves of oil and gas in the Middle East and especially Iran have obtained advantages for these countries. Therefore, the purpose of this study is modeling and forecasting the price of petrochemicals in the Middle East through GMDH neural network method. We focus on a single product, ammonia, which is a petrochemical with natural gas feedstock for which two approaches are taken: fundamental analysis for long-term and technical analysis for short-term. The findings suggest that the natural gas price have important effects on the price of ammonia in the Middle East, despite low natural gas price for ammonia producers in this region. Modeling through technical analysis is satisfactory because of smooth trend line of ammonia price. In the end, it is shown that the GMDH neural network has better predictive power than ARIMA method in predicting ammonia price based on error criteria.

The The purpose of this article is to analyze the resource curse or the very inverse relation of dependence to natural resources and growth rate in oil exporting countries. To estimate the model, Unbalance Panel Data is used from 1990 to 2004. The explanatory variables in this model include natural resources abundance Index, corruption Index, physical investment, terms of trade, and Education Index. The results show that oil income, as an Index of resource abundance, is individually not harmful on economic growth, i. e. the direct effect of oil income is positive on economic growth. However, when other explanatory variables such as corruption, physical investment, terms of trade, and education are entering the model, the total effect of oil income on economic growth is estimated as negative because of the oil income effect on the variables and then on economic growth. The results, moreover, show that the physical investment increase, terms of trade growth, and education expansion has a positive effect, and corruption Index a negative one on economic growth of oil exporting countries. At the end of the article, the effect of being present in the OPEC on the members' economic growth is analyzed that the results show the negative results of that on the economic growth of the members.

Productivity plays a significant and effective role in production growth and enhancement of competitiveness. The degree of performance of different economic sectors in using productive resource can be studied through measurement and analysis of productivity. In this paper, we have surveyd the sources of total factor productivity (TFP) growth in Iran’s industrial sector for the period of 1350-1385 by using an econometric approach. Using the Seemingly Unrelated Regression Equation (SURE) approach, we have initially estimated a Translog Cost Function and a system of cost share equations. Then, based on the estimation results of the above models, we have decomposed total factor productivity growth rate into the contributions of technological change and economies of scale. The results show that the share of technological change in productivity growth is greater than that of scale economies.

One of the main important issues in financial management is choosing the best way for utilizing investment. Investors like to invest their capitals in a way to minimize its risks. Bankruptcy is one of the risk factors which affect the decision making for investment. Predicting bankruptcy can helps investors to reduce the risks in capital markets and to recognize the best opportunities for their capitals. To do this, the Artificial Neural Network technique can be used for bankruptcy of firms in the future. In this study, this method is applied for the firms in the Province of Kerman, Iran. The result shows that none of the firms will bankrupt in the year 2008.

Prediction of inflation is of great importance for policy making. This need has led to extensive application of various models to forecast inflation rate. The competition between alternative applied forecasting models has led to further development in this area. The present study has applied artificial neural networks for prediction of inflation rate. The applied neural network is a multi-layer based on time series inflation approach during 1959-2007 using different algorithms of back propagation. To evaluate the artificial neural network performance, the prediction error criteria has been used. The findings of the research showed that the most appropriate networks are those based on Levenberg-Marquard training algorithm in which neurons in the hidden layers use nonlinear activation function s, and in the output layer use linear activation functions, and the number of neurons in each layer is chosen optimally. According to this preferred network, the inflation rates are predicted to be 10.59-21.99 during 2008-2012.