ARIMA MODELS IN THE SHORT-TERM FORECASTING OF INTERNAL MIGRATION IN RUSSIA
Abstract
Based on the data on inflows and outflows for the federal districts and types of settlements in the Russian Federation in 2000-2015 a statistical test of the hypothesis of the inertia of internal migration was carried out. The coefficients of autocorrelation were determined and autocorrelation dependence was found in 243 out of 256 time series, which made it possible to conclude that there is inertia in the internal migration of Russia. On the example of analysis of 28 time series of data on external migration to the countries of the European Union and 3 member states of the North American Free Trade Agreement for 2000-2015, the coefficients of autocorrelation were calculated and their statistical significance was estimated. It allowed to verify and confirm the potential applicability of the ARIMA method for short-term forecasting of international migration. The results of the selection of ARIMA models parameters for time series of internal migration indicators in Russia are summarized. Based on the retrospective forecasts of internal migration in Russia for the period 2013-2015, the forecasting errors using ARIMA models and the method of exponential smoothing are compared. It is concluded that ARIMA models provide more accuracy of short-term adaptive forecasts of internal migration in Russia in comparison with the method of exponential smoothing. Based on the calculation of confidence intervals for the mean, it has been demonstrated that the ARIMA models with the same accuracy predict inflows and outflows for two types of settlements (urban and rural). It was proved that ARIMA models can be used for short-term forecasting of internal migration of other states (on the example of data on the volumes of internal migration of the Republic of Belarus for 2000-2016).
References
1. Moiseenko V.M. Vnutrennyaya migratsiya naseleniya [Internal migration of the population]. Moscow, TEIS Publ., 2004. 285 p. (In Russ.).
2. Zaionchkovskaya Zh.A. Vnutrennyaya migratsiya v Rossii i v SSSR v XX veke kak otrazhenie sotsial’noi modernizatsii [Internal migration in Russia and the USSR in the XXth century as a reflection of social modernization]. Universe of Russia, 1999, no. 4, pp. 22-34. (In Russ.).
3. Vakulenko E.S., Mkrtchyan N.V., Furmanov K.K. Modelirovanie registriruemykh migratsionnykh potokov mezhdu regionami Rossiiskoi Federatsii [Modeling registered migration flows between regions of the Russian Federation]. Applied Econometrics, 2011, vol. 21, iss. 1, pp. 35-55. (In Russ.).
4. Vasilenko P.V. Primenenie gravitatsionnoi modeli dlya analiza vnutrioblastnykh migratsii na primere Novgorodskoi i Pskovskoi oblastei [Applying gravity model to interregional migration analysis in relation to Novgorod and Pskov region]. Pskovskii regionologicheskii zhurnal, 2013, no. 15, pp. 83-90. (In Russ.).
5. Soboleva S.V. Demograficheskie protsessy v regional’nom sotsial’no-ekonomicheskom razvitii [Demographic processes in the regional socio-economic development]. Novosibirsk, 1988. (In Russ.).
6. Isard W. Methods of regional analysis: An introduction to regional science. Cambridge, 1960 (Russ. ed. Izard U. Metody regional’nogo analiza: vvedenie v nauku o regionakh. Moscow, Progress Publ., 1966. 660 p.).
7. Balan M., Vasile V., Stanescu M. Gravity model application to estimate Romanian migration size. Knowledge horizons, 2013, no. 2, pp. 74-78. (In Russ.).
8. Taylor J.E. Differential migration, networks, information and risk. In: O. Stark (ed.). Migration, human capital, and development. Greenwich, CT, JAI Press, 1986. P. 147-171.
9. Massey D.S. et al. Theories of international migration: Review and appraisal. Population and Development Review, 1993, vol. 19, pp. 431-466.
10. Hatton T.J., Williamson J.G. The age of mass migration: Causes and economic impact. Oxford, Oxford University Press, 1998.
11. Faist T. The volume and dynamics of international migration and transnational social spaces. Oxford, Oxford University Press, 2000.
12. Pries L. Migration and transnational social spaces. Ashgate, Aldershot, 1999.
13. Stalker P. The no-nonsense guide to international migration. Oxford, New Internationalist, 2008 (Russ. ed. Stalker P. Mezhdunarodnaya migratsiya. Moscow, Knizhnyi Klub Knigovek Publ., 2015. 192 p.).
14. IMF/World Bank. Market access for developing countries’ exports. Paper prepared by the staffs of the IMF and the World Bank, 2001.
15. Virtual champions: India’s IT stars are still rising fast. The Economist, 1 June, 2006.
16. Freeman R., Oostendorp R. Wages around the world: Pay across occupations and countries. Working Paper 8058. Cambridge, MA, National Bureau of Economic Research, 2000.
17. Öberg S., Wils A.B. East-West migration in Europe. Can migration theories help estimate the numbers. Popnet, 1992, vol. 22, pp. 1-7.
18. Kupiszewski M. Modelowanie dynamiki przemian ludności w warunkach wzrostu znaczenia migracji międzynarodowych [Population dynamics modeling under the increasing importance of international migration]. Warsaw, IG&SO PAS, 2002. (In Polish).
19. I.I. Eliseeva (ed.). Statistika: ucheb. [Statistics: Textbook]. Moscow, Prospect LLC Publ., 2010. 448 p.
20. Box G.E.P., Jenkins G.M. Time series analysis. Forecasting and control. San Francisco: Holden Day., 1970. (Russ.ed. Boks Dzh., Dzhenkins G. Analiz vremennykh ryadov. Moscow, Mir Publ., 1974. 406 p.).
21. Greene W.H. Econometric analysis. Upper Saddle River, N. J., Prentice Hall, 2000.
22. Alho J. A stochastic forecast of the population of Finland. Reviews 1998/4. Helsinki, Statistics Finland, 1998.
23. Keilman N., Pham D.Q. Empirical errors and predicted errors in fertility, mortality and migration forecasts in the European Economic Area. Discussion Paper No. 386. Social and Demographic Research. Oslo, Statistics Norway, 2004.
24. Keilman N., Pham D.Q., Hetland A. Norway’s uncertain demographic future. Oslo, Statistics Norway, 2001.
25. Gorbey S., James D., Poot J. Population forecasting with endogenous migration: an application to trans-Tasman migration. International Regional Science Review, 1999, vol. 22, pp. 69-101.
26. Lawrence R.C. Forecasting U.S. mortality: a comparison of Box-Jenkins ARIMA and structural time series models. The Sociological Quarterly, 1996, vol. 37, pp. 127-144.
27. Keilman N., Pham D.Q., Hetland A. Why population forecasts should be probabilistic - illustrated by the case of Norway. Demographic Research, 2002,vol. 36pp. 409-454.
28. Hanke J.E., Wichern D.W., Reitsch A.G. Business forecasting. Upper Saddle River, N. J., Prentice Hall, 2000. (Russ. ed. Khank Dzh., Uichern D., Raits Dzh. Biznes-prognozirovanie. Moscow, Williams Publ. House, 2003. 656 p.).
Review
For citations:
Pavlovskij E.V. ARIMA MODELS IN THE SHORT-TERM FORECASTING OF INTERNAL MIGRATION IN RUSSIA. Voprosy statistiki. 2017;1(10):53-63. (In Russ.)
Views:
1128
Email this article 