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Showing 4 results for Gholizadeh
Asset Allocation Strategy under Housing Market
Aliakbar Gholizadeh, Mohsen Ebrahimi, Behnaz Kamyab,
Volume 6, Issue 21 (10-2015)
Abstract


In this study, by applyig a combination of Autoregressive Conditional Heteroskedasticity  and stochastic differential equations Models with Markowitz model we estimate the optimal portfolio investment in the housing market are discussed. For this purpose, use of assets, stock prices, housing prices, the price of coins and bonds during the period 1999-2013 with the monthly data. Autoregressive Conditional Heteroskedasticity  Models and stochastic differential equations results as input variables used to estimate the optimal portfolio Markowitz. Mean-variance analysis shows that during the real estate boom, housing as the dominant assets in risky assets and the largest share of funds to be allocated. During recent periods of recession as the housing sector, the housing of the optimal portfolio investment abroad and instead of stocks and investment coins in the basket of assets is considered dominant. Generally, bonds as risk-free assets in all periods as a reliable asset in the portfolio is considered optimal investor.


Estimating Spatial Concentration of Industry and Effective Factors on It Between Provinces of Iran
Nader Mehregan, Mohammad Hassan Fotros, Ali Akbar Gholizadeh, Younes Teymourei,
Volume 7, Issue 24 (6-2016)
Abstract

This paper considers spatial distribution of industrial activities and effective factors on such a distribution. Ellison and Glaeser’s spatial concentration index, has been used for measureing spatial distribution of industry. This index has been calculated by Ad-value variable for 30 provinces of Iran and for period of 2006-2013. So, The spatial panel data model has been used in order to stimate impact of effective factors on spatial concentration. Results of this paper, show that distribution of industrial activities between provinces is strongly unequal. Azarbaijan Sharghi, Markazi, Ghazvin and Tehran provinces by 0.03, 0.04, 0.05 and 0.06 for EG index, are the most industrial provinces respectively. Boushehr, Hormozgan and Ilam provinces with 0.68, 0.28 and 0.26 for EG index are the worst industrial provinces. Also, Results from estimating model show that spatial dependance of provinces is equal to 0.31. Increasing return to scale and transportation costs, each one by 0.07 and 0.001 for coefficient in model, are effective on spatial distribution of industry.


Investigation the Consumer Choice in the Use of Dental Services Care Applying Hekman Two-Step Procedure
Mohammad Amin Kouhbor, Majid Aghaei, Mahdieh Rezagholizadeh,
Volume 9, Issue 34 (12-2018)
Abstract
Considering the health importance in development process of countries, this study investigates factors affecting various types of dental care services participation and related expenditures as one of the most important aspects of oral health. For this reason, a sample of almost 40000 Iranian households in 2016 is selected and the impact of the mentioned factors analyzed using Heckman’s two-stage model. Results indicate that household’s income and education are two importance factors that affect the choice of dentist services and their related expenditure especially in Luxury dental services such as orthodontics and Gum regenerations. Income elasticity of root canal, Inspection and dental extraction are computed 1.04 and 0.0004 respectively. Finally, insurance coverage elasticity of root canal is 0.6, while the same elasticity for inspection is computed about 0.1 and -1 for dental extraction.

Impact of Economic Complexity and Political, Economic, Financial Risk Shock on Income Gap; Application of PVAR Model
Mohsen Tartar, Hamid Sepehrdoust, Ali Akbar Gholizadeh,
Volume 12, Issue 45 (11-2021)
Abstract
The status of income distribution is economically important because other macroeconomic variables, especially savings rates, affect the amount of investment and aggregate demand in different markets, and are politically a measure of government efficiency in attracting voters. The present study aims to investigate the macroeconomic variables affecting inequality in income distribution in the two groups of middle-income countries and high-income countries based on the International Monetary Fund classification. For this purpose, the annual data of economic complexity, scientific productivity, political risk, economic risk, and financial risk and the period 2019-2000 and the panel method have been used. The results show that in high-income countries, increasing economic complexity and scientific productivity reduces income inequality, while in middle-income countries, increasing scientific productivity reduces income inequality, but increasing economic complexity increases income inequality. Reducing political risk in both groups reduces income inequality; While reducing financial risk reduces income inequality in high-income countries, it increases income inequality in middle-income countries. The impact of economic risk on income inequality is also negligible in high-income countries, while in middle-income countries the impact of economic risk on income inequality is very strong, and reducing economic risk in this group of countries strongly reduces income inequality.

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