DOI: https://doi.org/10.18371/fcaptp.v3i26.144282

USING MODELS OF ONE TIME SERIES FOR ECONOMIC INDICATORS' FORECASTING IN MODERN CONDITIONS

V. G. Semenova., K. D. Semenova

Abstract


The main statistical methods of forecasting based on the data of a one-time series are investigated in the paper. Peculiarities of trend models use for prediction are considered. It is noted that in modern conditions of using computer programs, the choice of the trend form for forecasting is considerably simplified: it is possible to build different forms of the trend for one time series and to choose from them the one which best describes the output series according to mathematical criteria. However, such an approach can sometimes lead to a simple formalization of a rather complicated issue of forecasting. It has been established that in each special case, the choice of a particular model form for forecasting depends on the nature of the investigated indicator, the available information and the predicted tasks, but it is better to refuse to develop a detailed mathematical model in favor of a model with a small number of internal elements. The advantages and disadvantages of forecasting on the basis of autoregressive models are shown. A system of principles the observance of which can contribute to avoiding errors in the choice of forecasting tools is proposed. The analysis of dynamics and forecasting Ukraine’s gross domestic product per capita is calculated on the basis of trend and autoregression models. It is noted that in today's unstable conditions of economic development the complexity of carrying out the forecasting of economic indicators is increasing. Therefore, it is advisable to use not standard but adaptive methods for forecasting one-dimensional time data, the main property of which is to change the coefficients of the model when new information is received. At the same time, predictions that are made with the using of mathematical methods of modeling should be supplemented by expert assessments. The method of adaptive forecasting of economic indicators with the use of indicator analysis is improved. It is proposed to use both factors that are closely related to the investigated indicator and indicators that "warn" about future changes in the external or internal environment of the enterprise as indicators.


Keywords


economic indicator; trend; forecast; trend model; autoregressive model; indicator

References


Bomhoff, E. J. (1977). Predicting the money multiplier: A case study for the U.S. and the Netherlands. Journal of Monetary Economics, 3 (3), 325—345.

Watson, M. W. (2001). Time Series: Economic Forecasting. International Encyclopedia of the Social & Behavioral Sciences, 15721—15724.

Dickey, D. A., & Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. JASA. Journal of the American Statistical Association, 74(366).

Clements, M. P., & Hendry, D. F. (1998a). Forecasting economic processes. International Journal of Forecasting, 14, 111—131.

Hudakova, M., & Adamko, J. (2016). Technical reserves in insurance and Slovak insurance market. Economic annals-XXI, 162, 98—103 [in English].

Frenkel, A. A. (1989). Prognozirovanie proizvoditelnosti truda: metodyi i modeli [Prediction of labor productivity: methods and models]. Moscow: Economics [in Russian].

Chetyirkin, E. M. (1975). Statisticheskie metodyi prognozirovaniya [Statistical methods of forecasting]. Moscow: Statistics [in Russian].

Klebanova, T. S., & Rudachenko, O. O. (2015). Prohnozuvannia pokaznykiv finansovoi diialnosti pidpryiemstva zhytlovo-komunalnoho hospodarstva za dopomohoiu adaptyvnykh modelei [Forecasting indicators of enterprise of housing and communal services’ financial activity using adaptive models]. Biznes-informBusiness іnform, 1, 143—148 [in Ukrainian].

Kulynych, R. O. (2007). Sposoby prohnozuvannia ekonomichnykh yavyshch metodom statystychnykh rivnian zalezhnostei [Methods of forecasting economic phenomena by the method of statistical equations of dependencies]. Universytetski naukovi zapyskyUniversity scientific notes, 3 (23), 295—305 [in Ukrainian].

Tkachenko, I. S., & Proskurovych, O. V. (2017). Ekonomiko-matematychne modeliuvannia finansovoho rezultatu pidpryiemstva [Economic and mathematical modeling of the financial result of the enterprise]. Ekonomika: realii chasu Economics: realities of time, 3 (31), 84—94 [in Ukrainian].

Derzhavna sluzhba statystyky Ukrainy [Official page of the State Statistics Service of Ukraine]. ukrstat.gov.ua. Retrieved from http://www.ukrstat.gov.ua [in Ukrainian].

Guo, Y., Ran, C., Li, Х., & Ma, J. (2012). Adaptive online prediction method based on LS-SVR and its application in an electronic system. Journal of Zhelang University-Science c-computers & Electronics, 12, 881—890.


GOST Style Citations


Bomhoff E. J. Predicting the money multiplier: A case study for the U. S. and the Netherlands / E. J. Bomhoff // Journal of Monetary Economics. — 1977. — № 3 (3). — Р. 325—345.

Watson M. W. Time Series: Economic Forecasting / M. W. Watson // International Encyclopedia of the Social & Behavioral Sciences. — 2001. — Р. 15721—15724.

Dickey D. A. Distribution of the Estimators for Autoregressive Time Series With a Unit Root / D. A. Dickey, W. Fuller // JASA. Journal of the American Statistical Association. — 1979. — Vol. 74. — 74 р.

Clements, M. P. Forecasting economic processes / M. P. Clements, D. F. Hendry // International Journal of Forecasting. — 1998. — Vol. 14. — Р. 111—131.

Hudakova M. Technical reserves in insurance and Slovak insurance market / M. Hudakova, J. Adamko // Economic annals-XXI. — 2016. — № 162. — P. 98—103.

Френкель А. А. Прогнозирование производительности труда: методы и модели / А. А. Френкель. — Москва : Экономика, 1989. — 214 с.

Четыркин Е. М. Статистические методы прогнозирования / Е. М. Четыркин. — Москва : «Статистика», 1975. — 184 с.

Клебанова Т. С Прогнозування показників фінансової діяльності підприємства житлово-комунального господарства за допомогою адаптивних моделей / Т. С. Клебанова, О. О. Рудаченко // Бізнес-інформ. — 2015. — № 1. — С. 143—148.

Кулинич Р. О. Способи прогнозування економічних явищ методом статистичних рівнянь залежностей / Р. О. Кулинич // Університетські наукові записки. — 2007. — № 3 (23). — С. 295—305.

 Ткаченко І. С. Економіко-математичне моделювання фінансового результату підприємства / І. С. Ткаченко, О. В. Проскурович // Економіка: реалії часу. — 2017. — № 3 (31). — С. 84—94. 

 Державна служба статистики України [Електронний ресурс]. — Режим доступу : http://www.ukrstat.gov.ua.

 Guo Y. Adaptive online prediction method based on LS-SVR and its application in an electronic system / Y. Guo, C. Ran, X. Li, J. Ma // Journal of Zhelang University-Science c-computers & Electronics. — 2012. — Vol. 12. — P. 881—890.

 





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ISSN (print) 2306-4994, ISSN (on-line) 2310-8770