Introduction: Because of the uncertainty in economic growth theory and various alternative criteria for influencing factors on economic growth, as well as the lack of specification of the most appropriate econometrics model to investigate the influencing variables on economic growth, economic researchers have introduced new regression models to more accurately identify factors influencing economic growth. Materials and Methods: The current study aims to identify the factors affecting economic growth and the spatial correlation in 21 selected developed countries during 2005-2013 using panel Tobit and spatial panel Tobit models. Findings: The evaluation of used econometrics models showed that the spatial panel Tobit-spatial error model presented more appropriate results. Results showed that population, research and development expenditures, foreign direct investment, and life expectancy had a positive and significant effect on economic growth. Conclusion: Due to the superiority of the results obtained from the spatial Tobit panel model compared to the Tobit panel model, it is suggested to use the spatial econometric models along with traditional techniques in future studies regarding the economic growth of neighbouring countries to achieve more reliable results.

In this investigation, first the equation of food demand was estimated using an almost ideal demand system (AIDS) and Iranian cross sectional survey of income expenditure in 1384. Because of the cross sectional form of the data, the prices have calculated as marginal or unit values. Then with applying the elasticities of food categories, nutrient elasticities have extracted using Lancaster technology of consumption. These elasticities are useful to analysis the effects of aid policy on food demand and nutrient consumption respectively, and show the effects of the change in one food category price or food expenditure on the availability of each nutrient. In this article, as regard as demand equation is blocked-recursive simultaneous system, and zero value are existed as dependent variable, we use seemingly unrelated regressions (SUR) and tobit model.In another section, effects on demographic variables on food prices are examined. These variables are: age, education, region, urban or rural and Percapita income. Results have shown more food expenditure causes more food quality of food consumption and education, household size and other demographic variables are often effective on the quality of food.

The results of predicting employee absenteeism based an Ordinary Least Square (OLS) have been criticized. The present research compared the results from predicting absenteeism based on four varibales of job involvement, absenteeism compared to others, stress from life events, and number of children with the OLS and Tobit regression models. A sample of 197 male employees of factories in Esfahan city mean age: 28, SD : 10 responded to the Life Events Stress Inventory (Thoits, 1981), The Absenteeism Compared to Others Inventory (Baba, 1990), and the Job Involvement Inventory (Lodahl & Kejner, 1965). Tobit model analysis predicted employee absenteeism more precisely and explained a furhter 13% of the variance (R2 = 42.1) than did OLS.

One of the main goals of many households is to improve the quality of their children by increasing their education expenditure. This study investigates the factors affecting on education expenditure as measure of quality of children by using the Households’ Income and Expenditure survey dataset over 2010-2014 and utilizing Tobit models. The result of Tobit shows that with addition of one person to the number of children, amount of education expenses per child reduces about 0. 0064 billion Rial. But the effect of increasing one unit in the years of education of the head and mother of the household increases the educational expenditures by as much as 0. 038 and 0. 0548 billion Rial respectively. Therefore Becker’ s theory of child qualityquantity tradeoff is confirmed, so the household has tendency increase educational expenditure per child and improve the quality of child by decreasing the number of children. Therefore, given the increasing quality of children, the development of production infrastructure for employing highquality labor is an important step in increasing labor productivity and economic development in macro-level.

Since electricity distribution companies operate in various environmental conditions, their relative efficiency scores used for regulation purposes, should be corrected for environmental factors that could influence the underlying efficiency of them. This paper conducts efficiency correction for 39 Iranian electricity distribution companies applying two stage (DEA and Tobit) analysis in 2015. Accordingly, first to the efficiency performances of the electricity distribution companies are determined using DEA. In the second stage, the Tobit model is emploied to determine the environment factors which may explain the calculated efficiency scores. Based on the results, the rainfall level and the customer mix have negative and positive effects on efficiency of electricity distribution companies, respectively. Hence, the primary efficiency scores of DEA are corrected for environmental influences. The comparison of the primary and the corrected efficiencies indicate a significant changes in the efficiency scores and the ranking of the Iranian electricity distribution companies, such that the efficiency of companies with higher rainfall and lower industrial customers relative to their average, have increased, and vice versa.

This paper is about to estimate the health insurance demand function in the Iranian urban area. Considering to the censored data used in this study, in the outset, a simple Tobit model has been used to avoid the censoring bias. One of the main weaknesses of the Tobit analysis appears in presence of heteroskedasticity. To measure this weakness’ significance, the demand model has also been estimated using quantile regressions, namely Powell’s estimators, and coefficients at different quantiles has been compared with those form the simple Tobit estimate. Besides, the comparisons between coefficients confidence intervals generated using the Boot-strap method for relevant quantils and simple Tobit model’s confidence intervals do not imply a serious heteroskedasticity problem. According to estimation results, the income elasticity of private health insurance demand is positive, characterizing it as a normal service. Controlling for other factors, consumers evaluate private health insurance a substitution for its public counterpart and household’s demand for this service is a concave function of household head’s age so that the demand is maximized at 55 years old. A similar story applies to the relationship between the demand for private health insurance and household head’s education level such that earning a master’s degree maximizes the demand. The wealth level, measured by some proxies in this study, carries a significant positive effect on the demand. Estimations also imply that households set their private health insurance demand based on expected medical costs. One of the interesting results, which can derive other researches in this area, is the fact that households with retired heads significantly demand more private health insurance than others.

The purpose of this study is designing a model based on Tobit regression, DEA, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to evaluate the efficiency and also benchmarking the efficient and inefficient units. This model has three stages, and it uses the data envelopment analysis combined model with neural network, optimized by genetic algorithm, to evaluate the relative efficiency of 16 regional electric companies of Tavanir. A two-staged approach of data envelopment analysis and Tobit regression has been used to measure the effects of environmental variables on the mean efficiency of companies. Finally we use a hybrid model of particle swarm algorithm and genetic algorithm to benchmark the efficient and inefficient units. The mean efficiency of regional electric companies have increased from 0. 8934 to 0. 9147, during 2012 to 2017, and regional electric companies of Azarbayjan, Isfahan, Tehran, Khorasan, Semnan, Kerman, Gilan and Yazd, had the highest mean efficiency of 1, and west regional electric companies and Fars had the lowest efficiency of 0. 7047 and 0. 6025, respectively.