فصلنامه تحقیقات مدلسازی اقتصادی
سال دوم شماره 8 (تابستان 1391)

There are a lot of techniques and methods for prediction of bankruptcy; among them “Statistical methods” or econometrics techniques are more popular. As dependent variable in our study is qualitative; it is convenient to use qualitative discrete models. Mixed Logit model is one of the powerful and flexible techniques of discrete choices that allow the coefficients to be random with distribution function. Explanatory variables are financial ratios which derived from Zmijewski’s model. The sample data are from Tehran Stock Exchange’s Brokerage Companies during 2001-2008. We selected two random samples, one for estimation and another for prediction power test. Results show that the degree of successfulness of the model is over 90 percent.

This research aims to analyze the effects of industrial agglomeration on regional economic growth in the Iranian provinces. For this aim, this study is divided into theoretical and applied sectors. In the theoretical point of view, the research has proposed a simple theoretical framework to study the impacts of industrial agglomeration on regional economic growth. In applied sector, we have specified econometrics models and estimated them by using a system of simultaneous equations using Panel Data for 28 provinces of Iran over the period 2000-2006. Results show that regional economic growth is positively affected by industrial agglomeration and regional knowledge level and negatively affected by human capital mobility cost and per capita income. Results also show that regional economic growth, transportation cost, household expenditure and human capital mobility cost have positive effects on industrial agglomeration in the Iranian provinces.

D’Alessandro’s (2010) model investigates the impact of total government spending on private consumption; but according to Barro’s (1981) suggestion, the impact of two groups of government spending on private consumption can be studied separately. The fist group produces utility affecting services for household and the second group is as an input in the private production process. So in the present article, we use d’Alessandro’s (2010) framework -after some changes in household utility function and the production function- for estimating the separate effects of two groups of government spending on private consumption. In the next step, the data for Iran (1959-2007) is considered and the estimation results show that the first group of government spending for household consumption in short run is Edgeworth complement and in long run is Edgeworth independent. While government spending in case of the second group has a positive relationship with household consumption both in long run and short run. Thus, this paper proposes particular attention to changes in the composition of government spending in favor of government consumption spending as an input (second group) rather than expenses affecting the utility of households.

It is more likely that the monetary policy in Iran is discretionary and not based on a rule or a target. Besides, what is clear is that there have been explicit targets for inflation and economic growth in all five-year development plans (except the fifth plan). However, the question is that do policy makers observe the targets of development plans? Using an adjusted New Keynesian DSGE model for Iran, in this study we investigate the monetary policy under fiscal dominance and implicit inflation targeting of Iran. The results show that in most plans monetary authorities do not observe the explicit targets of five-year plans. The estimated monetary reaction function is only able to explain the period 2001-2011. The other result is that implementation delays of public projects have considerable effects on output and private consumption.

This study investigates predictability, chaos analysis, wavelet decomposition and the performance of neural network models in forecasting the return series of the Tehran Stock Exchange Index (TEDPIX). For this purpose, the daily data from April 24, 2009 to May 3, 2012 is used. Results show that TEDPIX series is chaotic and predictable with nonlinear effect. Also, according to obtained inverse of the largest lyapunov exponent, we are able to predict the future values of the series up to 31 days. Besides, our findings suggest that multi-layer feed forward neural network model and fuzzy model based on decomposed data, are of superior performances in predicting the return series. It is worth mentioning that, among these models, MFNN reveals the best performance.

The purpose of this paper is to analyze the effects of inflation on real investment in Iran. After briefly reviewing the investment theories and their situation in Iran, we consider the determinants of investment by using annual data (1958-2009). Results of the Augmented Dickey- Fuller (ADF) test indicate that all of the variables appearing in model are I(1). So, the results of the threshold regression model indicate that real GDP, the trade openness index and inflation rate can influence investment. Results also show that the effect of inflation on investment follows an asymmetric adjustment process. The threshold level for the rate of inflation has been estimated 11.9 percent. If the annual rate of inflation exceeds this threshold level, it will have a negative impact on investment. But, if inflation remains below this level, not only the negative effect fades away but also rising prices can boost investment.

Despite the belief of many economic scholars, women have played a significant role in the industrialization process of communities. Unfortunately, although women have the necessary competence to accept society responsibilities, inappropriate behavior and discriminatory is imposed to them and this problem has caused the women's participation rate slower than the participation rate of men. As women's economic participation rate is the measure of progress and development in developed and developing nations, the issue of women's economic participation is of great importance. This study aims at estimating the women’s participation in Iranian economy using macroeconomic data and econometric method of panel data. Results show that women's economic participation rate in 28 provinces of Iran is affected by the main variable of wage rate and also by the control variables such as gross domestic production per capita, unemployment rate etc. Results also show that increasing the share of industrial and agricultural sectors in supplying new occupations has positive effect on women’s participation while an increase in the share of services sector in the supply of new occupations has a negative effect.