A MODEL FOR THE JOB DEMAND FORECASTING IN THE ARCTIC ZONE OF THE RUSSIAN FEDERATION BASED ON TIME SERIES

Zhanna PETUKHOVA

Professor, Department of Economics, Management and Organization of Production, Norilsk State Industrial Institute

zh-petukhova@ust-hk.com.cn

Mikhail PETUKHOV

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

mpetukhov@nanyang-uni.com

Igor BELYAEV

Senior Lecturer, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 belyaev@lund-univer.eu

Lyudmila BODRYAKOVA

Associate Professor, Department of Information Systems and Technologies, Norilsk State Industrial Institute

 ln-bodryakova@lund-univer.eu

Abstract

The Russian Federation is the largest country in the world, whose territory includes the Arctic regions. The area of the land territories of the Arctic Zone of the Russian Federation (AZRF) is approximately 3.700,000 km2. The population of the Arctic Zone of Russia is approximately 7 million people, which is equal to 5% of the population of the entire Russian Federation. The purpose of this study is to investigate and analyse regression models for predicting the time series of the number of jobs in the labour market of the Russian Federation, to select an adequate model characterised by a minimum average relative error and a maximum lead time, or to select several adequate models for different forecasting periods: short-term, medium-term and long-term. The study examines the possibilities of predicting the situation in the labour market of the Arctic Zone of the Russian Federation, the demand for specialists in various industries using regression models for forecasting a time series. The simulation was performed using the Statistica software. As a result of the conducted studies, adequate forecasting models were obtained in the time period from 01.01.2020 to 01.01.2021, taking into account the epidemiological situation in the country. Thus, the best model with the smallest error was determined.

Keywords: labour market, regression models, education, autocorrelation function, autoregression.

JEL classification: I15, J11, J01

 pp. 291-298

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GREAT EXPECTATIONS FOR TOURISM AND REGIONAL DEVELOPMENT IN ROMANIA: WHY ARE NOT THEY MET?

Tudorel ANDREI
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00, Fax: +4.021.319.18.99
andreitudorel@ase.ro

Constantin MITRUT
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00; Fax: +4.021.319.18.99
cmitrut@ase.ro

Daniela-Luminita CONSTANTIN
The Bucharest Academy of Economic Studies, 6, Romana Square, district 1, Bucharest, postal code: 010374, postal office 22, Romania, Phone: +4.021.319.19.00; Fax: +4.021.319.18.99
danielaconstantin_2005@yahoo.com

Bogdan OANCEA
“Nicolae Titulescu” University,Calea Văcăreşti, Nr. 185, Sector 4, postal code 040051, Bucharest, Romania, Phone: +4.021.330.90.32, Fax: +4.021.330.86.06
bogdanoancea@univnt.ro
(corresponding author)

Abstract
Despite the high potential of the Romanian tourism competitiveness and reducing interregional disparities, the results obtained in the last fifteen-twenty years are far below expectations. This paper aims to identify national and regional characteristics of tourism in Romania during the period 1990 to 2010 and to evaluate the most important factors that influenced foreign tourists’ arrivals in Romania and the departures of Romanian tourists abroad. As infrastructure is one of the main obstacles to tourism development we have used data from development regions in order to explore the changes in the concentration of accommodation capacities. We have developed econometric models estimated on panel data to assess the implications of road infrastructure development and accommodation capacity utilization on economic results of tourism. The results indicate the important relationship between the territorial distribution of road infrastructure and the concentration of accommodation capacity.

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