Izvestiya of Saratov University.

Economics. Management. Law

ISSN 1994-2540 (Print)
ISSN 2542-1956 (Online)

For citation:

Kopnova E. D., Rodionova L. A. The Statistical Approach to the Analysis and Forecasting of Demographic Data. Journal Izvestiya of Saratov University. Economics. Management. Law, 2016, vol. 16, iss. 3, pp. 306-315. DOI: 10.18500/1994-2540-2016-16-3-306-315

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Language: 
Russian
Heading: 
Management
Article type: 
Article
UDC: 
519.246.8; 314.1; 311.17
DOI: 
10.18500/1994-2540-2016-16-3-306-315

The Statistical Approach to the Analysis and Forecasting of Demographic Data

Autors: 
Kopnova Elena Dmitrievna, National Research University Higher School of Economics (HSE)
Rodionova Lilyay Anatolievna, National Research University Higher School of Economics (HSE)
Abstract: 

Introduction. Possibilities of application ARIMA-models to analysis and forecasting of demographic time series were considered in the article. Foreign studies had shown that the ARIMA-models give good results for forecasting indicators such as population, birth rates and death rates, life expectancy, along with the traditional demographic methods (cohort-component approach). Research technique. Box – Jenkins methodology of the analysis and forecasting of time series, particularly with regard to demographic data: total fertility rate in Russia (1990–2014), the number of marriages by months in Russia (2005–2015), total fertility rate in France (1740–2014) and the unemployment rate in Russia (1996–2016) was used in the work. ARIMA-, ARIMA- and ARIMA-models, depending on the nature of the dynamics of the studied indicators were analyzed. Results. The analysis had shown that the estimated ARIMA-models for the total fertility rate and number of marriages were adequate and had good statistical and prognostic properties. Forecasts were built on basis of the obtained models. In the case of long series availability of properties with a long memory processes have not been identified.

Key words: 
time series analysis
ARIMA-model
SARIMA-model
ARFIMA-model
statistical methods
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Received: 
20.06.2016
Accepted: 
19.07.2016