ADVANCED DEMOGRAPHIC SITUATIONS BASED ON LAG MODELS

Authors

DOI:

https://doi.org/10.20998/2079-0023.2023.02.09

Keywords:

economic modeling, economic analysis, time series, lag, dynamic models, population and migration processes

Abstract

Research and forecasting of time series based on models with lags is offered, as well as calculation of a reliable forecast based on data on birth rates in Ukraine. Economic modeling is one of the important modern tools for assessing the impact of technologies on the economic sector in order to obtain an optimal solution. Economic evaluations can be based on several different modeling approaches, each with its own strengths and weaknesses. The relevance of the use of economic and mathematical models for the purpose of studying demography is connected with the need to study population and migration processes, as well as for further planning and implementation of the country's economic and social development. In every sphere of the economy, there are phenomena that are interesting and important to study in their development, as they evolve over time. Prices, economic conditions, industrial processes, and demographic data tend to change over time. The set of measurements of this kind of indicators depending on time is a time series. The goals of studying time series can be different. It is possible, for example, to try to predict the future on the basis of knowledge of the past, to control the process that generates the series, to try to find out the mechanism underlying the process, to clear the series of components that obscure its dynamics, or simply to briefly describe the characteristic features of the series. When studying the relationships between indicators or when analyzing their development over time, not only the current value of the variables, but also some previous values in time, as well as time itself, are used as explanatory variables. Models of this type are called dynamic. In economic analysis, dynamic models are used quite widely. This is quite natural, because in many cases the influence of some economic factors on others is not carried out immediately, but with some delay − a lag. The object of research is the mathematical model of the interdependence of the vector time series "Births in Ukraine for January 2005 − July 2012." The data are chosen quite relevantly, because without a preliminary demographic forecast it is impossible to imagine the prospects of industry and consumption of goods and services, housing construction, development of social infrastructure, health care and education, pension system and solutions to geopolitical problems.

Author Biographies

Olena Ahiezer, National Technical University "Kharkiv Polytechnic Institute"

Candidate of Technical Sciences, Professor of NTU "KhPI", Head of the Department of Computer Mathematics and Data Analysis, National Technical University "Kharkiv Polytechnic Institute", Kharkiv, Ukraine

Oleg Tonitsa, National Technical University "Kharkiv Polytechnic Institute"

Candidate of Physical and Mathematical Sciences, Associate Professor, Associate Professor in the Department of Computer Mathematics and Data Analysis, National Technical University «Kharkiv Polytechnic Institute», Kharkiv, Ukraine

Oksana Gelyarovska, National Technical University "Kharkiv Polytechnic Institute"

Associate Professor at the Department of Computer Mathematics and Data Analysis, National Technical University «Kharkiv Polytechnic Institute», Kharkiv, Ukraine

Irina Serdyuk, National Technical University "Kharkiv Polytechnic Institute"

Associate Professor at the Department of Computer Mathematics and Data Analysis, National Technical University «Kharkiv Polytechnic Institute», Kharkiv, Ukraine

Mykola Aslandukov, National Technical University "Kharkiv Polytechnic Institute"

Senior Lecturer at the Department of Computer Mathematics and Data Analysis, National Technical University «Kharkiv Polytechnic Institute», Kharkiv, Ukraine

References

Rus'ka R. V. Ekonometryka: navchal'nyy posibnyk [Econometrics: a study guide]. Ternopil', Tayp Publ., 2012. 224 p.

Voloshyn O. R., Halayko N. V. Ekonometriya [Econometrics]. L'viv: L'vivs'kyy derzhavnyy universytet vnutrishnikh sprav Publ., 2012. 192 p.

Kushlyk-Dyvul's'ka O. I., Polishchuk N. V., Orel B. P., Shtabalyuk P. I. Teoriya ymovirnostey ta matematychna statystyka [Probability theory and mathematical statistics]. Kyyiv, NTUU "KPI" Publ., 2014. 212 p.

Lyubchyk L. Grinberg G., Lubchick M., Galuza A., Akhiiezer O. Interval Evaluation of Stationary State Probabilities for Markov Set-Chain Models. 10th International Conference on Advanced Computer Information Technologies (ACIT). Deggendorf, Germany, 2020, pp. 82–85. DOI: 10.1109/ACIT49673.2020.9208932

Dzubenko M. I., Kolenov I. V., Pelipenko V. P., Dakhov N. F., Galuza A. A. Pulse power supply unit with microcontroller control for a laser diode array pumped erbium-ytterbium laser. Telecommunications and Radio Engineering. 2020, vol. 79, issue 10, pp. 891–902. DOI: 10.1615/TelecomRadEng.v79.i10.60.

Hur'yanova L. S., Klebanova T. S., Serhiyenko O. A. Ekonometryka: navchal'nyy posibnyk dlya studentiv napryamu pidhotovky "Ekonomichna kibernetyka" usikh form navchannya. [Econometrics: a basic textbook for students directly preparing "Economic Cybernetics" for all forms of learning]. Kharkiv, KhNEU im. S. Kuznetsya Publ., 2015. 384 p.

Vasyl'kiv I. M. Osnovy teoriyi ymovirnostey i matematychnoyi statystyky [Basics of probability theory and mathematical statistics]. L'viv, LNU im. Ivana Franka Publ., 2020. 184 p.

Mlavets' Yu. Yu., Sharkadi M. M. Teoriya ymovirnostey i matematychna statystyka [Probability Theory and Mathematical Statistics]. Uzhhorod, DVNZ "UzhNU" Publ., 2015. 48 p.

Harder C. Ye., Kornil' T. L. Fraktal'nyy analiz ta prohnozuvannya tendentsiyi finansovoho chasovoho ryadu [Fractal analysis and trend forecasting of financial time series]. Visnyk Nats. tekhn. un-tu «KhPI»: zb.nauk. pr. Temat. vyp.: Matematychne modelyuvannya v tekhnitsi ta tekhnolohiyakh [Bulletin of the National Technical University "KhPI": a collection of scientific works. Thematic issue: Mathematical modeling in engineering and technology]. Kharkiv, NTU "KhPI" Publ., 2018, no. 3, pp. 37–40.

Nedashkivs'kyy Ye. A. Teoretyko-metodolohichni aspekty prohnozuvannya tymchasovykh ryadiv z fraktal'nymy vlastyvostyamy na osnovi linhvistychnoho modelyuvannya [Theoretical and methodological aspects of forecasting time series with fractal properties based on linguistic modeling]. Vcheni zapysky TNU imeni V.I. Vernads'koho. Seriya: tekhnichni nauky [Academic notes of TNU named after V.I. Vernadskyi. Series: technical sciences]. 2019, no. 2, pp. 155–160.

Kondratenko K. A., Harder S. Ye. Prohnozuvannya finansovoho chasovoho ryadu z vykorystannyam rekurentnoyi neyronnoyi merezhi [Financial time series forecasting using a recurrent neural network]. XIII Mizhnarodna naukovo-praktychna konferentsiya mahistrantiv ta aspirantiv NTU "KhPI" [XIII International scientific and practical conference of master's and postgraduate students of NTU "KhPI"]. Kharkiv, NTU "KhPI" Publ., 2019, pp. 66–67.

Shaposhnikova I. O. Analiz chasovykh ryadiv pervynnoho rynku zhytlovoyi nerukhomosti mista Kyyeva [Analysis of time series of the primary market of residential real estate in Kyiv]. Ekonomichnyy visnyk Kyyivs'koho natsional'noho universytetk budivnytstva i arkhitektury [Economic Bulletin of the Kyiv National University of Construction and Architecture]. Kyyiv, KNUBA Publ., 2018, no. 36/1, pp. 140–147.

Published

2023-12-19

How to Cite

Ahiezer, O., Tonitsa, O., Gelyarovska, O., Serdyuk, I., & Aslandukov, M. (2023). ADVANCED DEMOGRAPHIC SITUATIONS BASED ON LAG MODELS. Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, (2 (10), 60–66. https://doi.org/10.20998/2079-0023.2023.02.09

Issue

Section

MATHEMATICAL AND COMPUTER MODELING