Aim: This study estimated the GDP share of pharmaceuticals in Iran based on the drivers of pharmaceutical expenditure and compared it with that of 31 members of the Organisation for Economic Cooperation and Development (OECD). Subject and methods: The factors contributing to pharmaceutical expenditure were identified through literature review and studied by 8 experts to classify the factors. Then, using the panel data method, a model was built to estimate the GDP share of pharmaceutical expenditure based on the extracted factors of the selected countries in Iran’s model. To explain the observed differences, several regression analyses were performed based on cross-sectional data. The analyses were performed using EVIEWS software, version 10. Results: The explanatory variables for the selected countries in the panel model (R² = 0.98) were specified. Government health expenditure (β = 0.1432), the share of generic drugs (β = − 0.0143), gross domestic product (GDP) per capita (β = − 0.0058) and the rate of disability-adjusted life-years (DALY) (β = 0.0028) contributed most to pharmaceutical expenditure. In comparison, in the Iranian estimation model (R² = 0.84), government health expenditure (β = 0.0536) and the share of generic drugs (β = 0.0369) had a significant impact on pharmaceutical expenditure. In the estimation model with more estimators for Iran (R² = 0.99), government health expenditure (β = 0.1694), disease prevalence (β = 0.0537), the share of generic drugs (β = 0.0102), the DALY rate (β = 0.0039), GDP per capita (β = − 0.0033), and the drug price index (β = 0.0007) contribute most to pharmaceutical expenditure. Conclusion: In the models of the study, factors related to the structure of the healthcare system and the pharmaceutical system contributed most to pharmaceutical expenditure as a share of GDP. Moreover, disease profiles show its predictive role in the second model for Iran.

Studies show neural networks have better results in predicting of financial time series in comparison to any linear or non-linear functional form to model the price movement. Neural networks have the advantage of simulating the non-linear models when little a priori knowledge of the structure of problem domains exist or the number of immeasurable input variables are great and system has a chaotic characteristics. Among different methods, MLFF neural network with back-propagation learning algorithm and GMDH neural network with genetic learning algorithms are used to predict gas price of the Henry Hob database covering 01/01/2004-13/7/2009 period. This paper uses moving average crossover inputs and the results confirms (1) the fact there is short-term dependence in gas price movements, (2) the EMA moving average has better result and also (3) by means of the GMDH neural networks, prediction accuracy in comparison to MLFF neural networks, can be improved.

This paper surveys relation between government budget, foreign direct investment (FDI) and public capital efficiency role in the infrastructure sector for attracting FDI. To achieve this goal, dynamic optimization methods are used for extracting the growth path of public consumption expenditure of government within a neoclassical growth model framework. The results suggest that FDI has a direct and positive effect on the optimal growth rate of public consumption expenditure. In fact, if the government invests more in the infrastructure sector and it is more efficient to attract FDI, the optimal growth rate of public consumption will be higher in long run respect to short run. The equation for the growth rate of public consumption is derived and estimated on the basis of data during the period 1971 - 2014 in Iran.

This article reviews the relationship between the oil market and the stock market during the Corona outbreak. This study aims to analyze the stock market and the effect of oil prices on this market during the corona pandemic. The hypothesis of this paper is whether while oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes. Forecasting the stock market in each period has been difficult and the value of stock index has been affected by various factors. Among these factors has been the oil and gas sector, especially in countries dependent on the revenue from their sales. On the other hand, the outbreak of Covid-19 pandemic has led to profound changes in both areas. This study examines relationship between Brent oil price and Iran stock market Index during the outbreak of corona pandemic. Research method is, vector autoregression model (VAR) which using daily data covering the period from February 20, 2020 to August 21,2020. The findings of this study suggest that a negative causal effect from Brent oil price changes to the Iran stock market Index. Also, the results of impulse response functions and variance decompositions showed that some corona pandemic indicators have significant effects on the stock index.JEL Classification: I18, E44, Q4, C5

Introduction: Rapid industrialisation and urbanisation can worsen air quality and increase the financial pressure on health systems. However, city-level evidence on how fine particulate matter (PM2.5) and urban environmental characteristics relate to healthcare expenditures remains limited in middle-income settings. Methods: We conducted an ecological panel study of 10 industrial cities in Iran (2011–2024). We assembled city-year data on per-capita healthcare expenditures and key urban characteristics from municipal statistical yearbooks and linked them to annual PM2.5 estimates. We fitted log–log panel models with city random effects and year fixed effects using robust inference. Sensitivity analyses included leave-one-city-out re-estimation and an alternative specification replacing income with the household-income Gini coefficient. Results: Per-capita healthcare expenditures were positively associated with PM2.5 (elasticity=0.37; p=0.057), per-capita income (=0.18; p=0.020), population density (=0.28; p=0.010), and the share aged ≥60 years (=1.05; p=0.008). The association for green space was small and sensitive to specification (=0.05; p=0.034 in the baseline model and not statistically significant when lagged), while per-capita healthcare facilities were not statistically significant. In the inequality model, the Gini coefficient was positively associated with healthcare expenditures (p=0.002). Conclusions: Higher ambient PM2.5, urban density, population ageing and income inequality were associated with higher healthcare expenditures in this city-level panel analysis. These findings suggest that coordinated policies combining emission control, health-system strengthening (particularly preventive and primary care), and improved quality and equitable access to urban green infrastructure may help curb growth in healthcare spending in rapidly industrialising and urbanising settings.