IMPORT AND TELECOM SECTOR REVENUE IN EXPLAINING THE MOTIVES TO INVEST IN INNOVATIVE TECHNOLOGY BY ARMENIAN OPERATORS

By estimating the impact of real imports of innovative technologies and non-innovative equipment on the real revenue of the telecommunications operators; and the impact of real imports of non-innovative equipment and the real revenue of the telecommunications operators on the real revenue of the telecommunications operators in Armenia from the second half of 2014 till the end of 2020, we have identified which factors will mainly lead to the growth of the sector and force them to invest in innovative technology. According to the analysis of the econometric model #1, it can be said that companies increase the cost of importing innovative technologies and equipment by 6.11%, on average, as soon as revenue decreases by 1%. Secondly, import of non-innovative equipment, like phones, once every 24 months, could lead to an increase in import of innovative equipment. According to the model #2 the costs of importing non-innovative equipment have a statistically significant impact on the revenue of telecom companies, however, the import of new generation phones does not lead to a huge increase in the use of services offered by operators. 11 months after the introduction of innovative technologies, their impact on the revenue of the telecom companies becomes statistically significant and negative. We conclude that the decrease of telecoms revenue leads to the growth of real imports of innovative technologies. The model results will enable telecoms to overcome the situations that would decrease companies’ revenue, to revise the periods set for importing equipment and innovative technology.

Nonlinear Influence of Chinese Real Estate Development on Environmental Pollution: New Evidence from Spatial Econometric Model

Environmental pollution is a problem of universal concern throughout the globe. The development of real estate industry not only consumes huge resources, but also has close ties with high-consumption industries such as the construction industry. However, previous studies have rarely explored the impact of real estate development on environmental pollution. Therefore, this paper employs the entropy method to construct a comprehensive index of environmental pollution based on panel data of 31 provinces in China from 2000 to 2017, and empirically examines the impact of real estate development on environmental pollution. This article uses real estate investment to measure the development of the real estate industry. In view of the high spatial autocorrelation of environmental pollution, this paper selects a spatial econometric model. The empirical study found that: (1) By using the Spatial Durbin Model, real estate development has an inverted U-shaped impact on environmental pollution. Meanwhile, most cities have not yet reached the turning point; that is, with the continuous development of the real estate industry, environmental pollution will continue to increase. (2) Further regional heterogeneity found that the inverted U-shaped relationship still exists in coastal and inland areas. (3) Finally, this article used the Spatial Mediation Model to explain the nonlinear impact of real estate development on environmental pollution, with two important mediating variables: population density and industrial structure. Through the above analysis, it can be observed that real estate development has a significant impact on environmental pollution. Thus, the country and the government can reduce environmental pollution by improving the investment structure, using environmentally friendly building materials, guiding population flow and promoting industrial upgrading.

Spatio-Temporal Variation of Health Production Efficiency Considering Environmental Pollution in China Based on Modified EBM and Spatial Econometric Model

In order to strengthen the construction of China's health industry and improve the health of the people, based on the data of 31 provinces and cities in China from 2009 to 2019, the improved EBM model is used to measure the health production efficiency of each region, and Moran index is used to study the Spatio-temporal variation of health production efficiency of each province. Finally, the spatial econometric model is applied to study the influencing factors of the Spatio-temporal variation of health production efficiency. The results show that generally speaking, the average efficiency of 31 provinces and cities is above 0.7, and the average efficiency of some regions is above 1. From the perspective of time variation, the average efficiency value in the eastern region and the middle region increases from 0.816 to 0.882 and from 0.851 to 0.861, respectively. However, the average efficiency value in the western region and northeast region decreases from 0.861 to 0.83 and from 0.864 to 0.805, respectively. From the perspective of spatial distribution, HH agglomeration and LL agglomeration exist in most regions. By comparing Moran scatter plots in 2009 and 2019, it is found that the quadrants of most regions remain unchanged, and LL agglomeration is the main agglomeration type in local space. There is a significant spatial dependence among different regions. From the perspective of spatial empirical results, Pgdp, Med, and Pd have a positive effect on health production efficiency. The direct effect and indirect effect of Pgdp, Med, and Gov all pass the significance test of 1%, indicating that there are spatial spillover effects of the three indicators. Each region should reasonably deal with the spillover effect of surrounding regions, vigorously develop economic activities, carry out cooperation with surrounding regions and apply demonstration effect to accelerate the development of overall health production.

Does Manufacturing Agglomeration Promote Or Hinder Green Development Efficiency? Evidence From Yangtze River Economic Belt, China

Sustainable development can be mainly achieved by promoting the green transformation and development of the world economy and by improving the efficiency of regional green development, which often receive extensive attention from the academia. This paper uses a spatial econometric model to estimate the impact of manufacturing agglomeration on green development efficiency based on the panel data of China’s Yangtze River Economic Belt (YREB). The results show an overall large gap of green development efficiency between regions in the Yangtze River Economic Zone, mostly due to the extremely uneven development of green development efficiency in the upper reaches. Opposite to the middle and lower reaches, manufacturing agglomeration in the upper reaches of the YREB improves green development efficiency. Manufacturing agglomeration is conducive to the improvement of green development efficiency in neighboring areas. Nonetheless, it may hinder green development efficiency by inhibiting green technological innovation. This paper provides empirical evidence and policy implications for applying manufacturing agglomeration to promote green development efficiency in accordance with local conditions.

FORECASTING THE VOLUMES OF IMPORT OF LIVE ANIMALS IN THE RUSSIAN FEDERATION

This article provides an econometric analysis of the volume of imports of live animals in the Russian Federation according to the data of 01.01.2018-30.09.2021. by quarters. Based on the presented quarterly data, an econometric model was developed and a forecast for the volume of imports of live animals in the 4th quarter of 2021 was calculated

FORECASTING THE ECONOMIC PROCESS USING ECONOMETRIC ANALYSIS

The article considers the necessity of using econometric research methods in the analysis and forecasting of economic processes and phenomena. A concrete example of managerial decision making examined in the given article is based on the assumption of an econometric model and it’s ability to analyze the effectiveness of applying different types of advertising on the company's activities

Consumption Contribution to Economy Growth

The analysis of the final consumption influence on the Gross Domestic Product is rarely studied. The final consumption is one important studies that follow the expenses method of GDP calculation and formation. The econometric approach gives substantial results when a longer interval is approached. The fact of econometric model is the dependence of the national economy on final consumption. Thus, the unifactorial regression model can be used to establish the influence that the value of final consumption. The Researcher make some description about final consumption in Southeast Asia+3 and US.

The Use of Econometric Analysis in Forecasting Macroeconomic Processes

The article discusses the issues related to the influence of various price categories of goods on the level of inflation. The possibility of using econometric analysis for predictive purposes is considered. An econometric model of multiple regression of the influence of consumer prices on the level of inflation is formed. A linear multiple regression equation is constructed. The selection of factors for the construction of an econometric model is made. The main groups of goods are identified, the prices of which form the level of inflation and can have a primary impact. The viability of the model and the possibility of forecasting macroeconomic indicators based on econometric analysis are proved.