Forecasting the population of Brazil using the Box-Jenkins ARIMA approach

NYONI, THABANI (2019): Forecasting the population of Brazil using the Box-Jenkins ARIMA approach.

Abstract

Employing annual time series data on total population in Brazil from 1960 to 2017, we model and forecast total population over the next 3 decades using the Box – Jenkins ARIMA technique. Diagnostic tests such as the ADF tests show that Brazil annual total population is non-stationary in all levels; for simplicity purposes, the study has assumed that the POP series is I (2). Based on the AIC, the study presents the ARIMA (6, 2, 0) model as the optimal model. The diagnostic tests further indicate that the presented model is stable and that its residuals are stationary. The results of the study reveal that total population in Brazil will continue to rise in the next three decades and in 2050 Brazil’s total population will be approximately 256 million people. Four policy prescriptions have been suggested for consideration by the government of Brazil.

• Item Type: MPRA Paper
• Language: English
• Keywords: Brazil; forecasting; population
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R23 - Regional Migration ; Regional Labor Markets ; Population ; Neighborhood Characteristics

References

[1] Asteriou, D. & Hall, S. G. (2007). Applied Econometrics: a modern approach, Revised Edition, Palgrave MacMillan, New York.

[2] Ayele, A. W & Zewdie, M. A (2017). Modeling and forecasting Ethiopian human population size and its pattern, International Journal of Social Sciences, Arts and Humanities, 4 (3): 71 – 82.

[3] Beg, A. B. M. R. A & Islam, M. R (2016). Forecasting and modeling population growth of Bangladesh, American Journal of Mathematics and Statistics, 6 (4): 190 – 195.

[4] Center for International Research (1993). Population Trends – Brazil, US Department of Commerce, Economics and Statistics Administration, Bureau of the Census.

[5] Dominic, A., Oluwatoyin, M. A., & Fagbeminiyi, F. F (2016). The determinants of population growth in Nigeria: a co-integration approach, The International Journal of Humanities and Social Studies, 4 (11): 38 – 44.

[6] Du Preez, J. & Witt, S. F. (2003). Univariate and multivariate time series forecasting: An application to tourism demand, International Journal of Forecasting, 19: 435 – 451.

[7] Goh, C. & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic non-stationary seasonality and intervention, Tourism Management, 23: 499 – 510.

[8] Gragnolati, M., Jorgenson, O. H., Rocha, R & Fruttero, A (2011). Growing old in an older Brazil: implications of population ageing on growth, poverty, public finance and service delivery, World Bank, Washington DC.

[9] Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40.

[10] Nyoni, T (2018). Modeling and Forecasting Naira / USD Exchange Rate in Nigeria: a Box – Jenkins ARIMA approach, University of Munich Library – Munich Personal RePEc Archive (MPRA), Paper No. 88622.

[11] Nyoni, T. (2018). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.

[12] Song, H., Witt, S. F. & Jensen, T. C. (2003b). Tourism forecasting: accuracy of alternative econometric models, International Journal of Forecasting, 19: 123 – 141.

[13] Tartiyus, E. H., Dauda, T. M., & Peter, A (2015). Impact of population growth on economic growth in Nigeria, IOSR Journal of Humanities and Social Science (IOSR-JHSS), 20 (4): 115 – 123.

[14] Todaro, M & Smith, S (2006). Economic Development, 9th Edition, Vrinda Publications, New Delhi.

[15] United Nations (2015). World Population Prospects: The 2015 Revision, Key Findings and Advance Tables, Department of Economic and Social Affairs, Population Division, Working Paper No. ESA/P/WP/241.

[16] United Nations (2017). World Population Prospects: The 2017 Revision, Key Findings and Advance Tables, Department of Economics and Social Affairs, Population Division, Working Paper No. ESAP/P/WP/248.

[17] Zakria, M & Muhammad, F (2009). Forecasting the population of Pakistan using ARIMA models, Pakistan Journal of Agricultural Sciences, 46 (3): 214 – 223.