Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics
DOI:
https://doi.org/10.2478/eb-2020-0008Keywords:
ARMA/ARIMA, Population growth, Population projections, World populationAbstract
This paper forecasts the world population using the Autoregressive Integration Moving Average (ARIMA) for estimation and projection for short-range and long-term population sizes of the world, regions and sub-regions. The study provides evidence that growth and population explosion will continue in Sub-Saharan Africa, tending the need to aggressively promote pragmatic programmes that will balance population growth and sustainable economic growth in the region. The study argued that early projections took for granted the positive and negative implications of population growth on the social structure and offset the natural process, which might have implication(s) on survival rate. Given the obvious imbalance in population growth across continents and regions of the world, a more purposeful inter-regional and economic co-operation that supports and enhances population balancing and economic expansion among nations is highly recommended. In this regard, the United Nations should compel member states to vigorously and effectively implement domestic and international support programmes with this objective in view.References
Alders, M., & Beer, J. (2004). Assumptions on Fertility in Stochastic Population Forecasts. International Statistical Review, 72(1), 65–79. https://doi.org/10.1111/j.1751-5823.2004.tb00224.x
Alho, J. M., & Spencer, B. D. (1985). Uncertain population forecasting. Journal of the American Statistical Association, 80(390), 306–314. https://doi.org/10.1080/01621459.1985.10478113
Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry, 11(2), 1–17. https://doi.org/10.3390/sym11020240
Anderson, B. D. O., Deistler, M., & Dufour, J. M. (2019). On the Sensitivity of Granger Causality to Errors-In-Variables, Linear Transformations and Subsampling. Journal of Time Series Analysis, 40(1), 102–123. https://doi.org/10.1111/jtsa.12430
Astill, S., Harvey, D. I., Leybourne, S. J., Sollis, R., & Robert Taylor, A. M. (2018). Real-Time Monitoring for Explosive Financial Bubbles. Journal of Time Series Analysis, 39(6), 863–891. https://doi.org/10.1111/jtsa.12409
Barrus, R. (2007). Thomas R. Malthus, An Essay on the Principle of Population . Politics and the Life Sciences, 23(2), 75–77. https://doi.org/10.2990/1471-5457(2004)23[75:trmaeo]2.0.co;2
Bartholomew, D. J. (1971). Review Reviewed Work: Time Series Analysis Forecasting and Control. Operational Research Quarterly, 22(2), 143–144. https://doi.org/10.2307/3008255
Beare, B. K. (2018). Unit Root Testing with Unstable Volatility. Journal of Time Series Analysis, 39(6), 816–835. https://doi.org/10.1111/jtsa.12279
Ben Amor, S., Boubaker, H., & Belkacem, L. (2018). Forecasting electricity spot price for Nord Pool market with a hybrid k-factor GARMA–LLWNN model. Journal of Forecasting, 37(8), 832–851. https://doi.org/10.1002/for.2544
Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-DAy, San Francisco, 199–201.
Bratu, M. (2012). Econometric models or smoothing exponential techniques to predict macroeconomic indicators in Romania. Zagreb International Review of Economic & Business, 15(2), 87–100. Retrieved from http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=137486
Brooks, C. (2019). Introductory Econometrics for Finance. Cambridge Univeristy Press: USA.
Carter, J. R., & Narasimhan, R. (1996). Purchasing and Supply Management: Future Directions and Trends. International Journal of Purchasing and Materials Management, 32(3), 2–12. https://doi.org/10.1111/j.1745-493x.1996.tb00225.x
Corberán-Vallet, A., Bermúdez, J. D., & Vercher, E. (2011). Forecasting correlated time series with exponential smoothing models. International Journal of Forecasting, 27(2), 252–265. https://doi.org/10.1016/j.ijforecast.2010.06.003
Coshall, J. T., & Charlesworth, R. (2011). A management orientated approach to combination forecasting of tourism demand. Tourism Management, 32(4), 759–769. https://doi.org/10.1016/j.tourman.2010.06.011
Day, A. (2002). The Prospects of Cosmopolitan World Order. Global Social Policy, 2(200212), 295–318.
Dumont, G.-F. (2018). Urban demographic transition. Urban Development Issues, 56(4), 13–25. https://doi.org/10.2478/udi-2018-0009
Fernández-López de Pablo, J., Gutiérrez-Roig, M., Gómez-Puche, M., McLaughlin, R., Silva, F., & Lozano, S. (2019). Palaeodemographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia. Nature Communications, 10(1), 1872. https://doi.org/10.1038/s41467-019-09833-3
Galavi, V., & Brinkgreve, R. (2014). Finite element modelling of geotechnical structures subjected to moving loads. Numerical Methods in Geotechnical Engineering, (June), 235–240.
Gonçalves Mazzeu, J. H., Veiga, H., & Mariti, M. B. (2019). Modeling and forecasting the oil volatility index. Journal of Forecasting, 38(8). https://doi.org/10.1002/for.2598
Gorrostieta, C., Ombao, H., & Von Sachs, R. (2019). Time-Dependent Dual-Frequency Coherence in Multivariate Non-Stationary Time Series. Journal of Time Series Analysis, 40(1), 3–22. https://doi.org/10.1111/jtsa.12408
Goto, Y., & Taniguchi, M. (2019). Robustness of Zero Crossing Estimator. Journal of Time Series Analysis, 40(5). https://doi.org/10.1111/jtsa.12463
Hill, R. C., Griffiths, W. E., & Lim, G. C. (2011). Prinicples of Econometrics. John Wilay & Sons.
Hofmann, K. (2013). Beyond the principle of population: Malthus’s Essay. European Journal of the History of Economic Thought, 20(3), 399–425. https://doi.org/10.1080/09672567.2012.654805
Jebb, A. T., & Tay, L. (2017). Introduction to Time Series Analysis for Organizational Research: Methods for Longitudinal Analyses. Organizational Research Methods, 20(1). https://doi.org/10.1177/1094428116668035
Lal, M., Jain, A. K., & Sinha, M. C. (1987). Possible climatic implications of depletion of Antarctic ozone. Tellus B: Chemical and Physical Meteorology, 39(3), 326–328. https://doi.org/10.3402/tellusb.v39i3.15351
Li, Q., Reuser, M., Kraus, C., & Alho, J. (2009). Ageing of a giant: A stochastic population forecast for China, 2006–2060. Journal of Population Research, 26(1), 21–50. https://doi.org/10.1007/s12546-008-9004-z
Marshall, V. M., et. al. (2017). Social Well-Being in Northern Ireland: A Longitudinal Study 1958-1998. Biological Conservation, 44(0), 1–12.
Notestein, F. W., Taeuber, I. B., Kirk, D., Ansley, J., Kiser, L. K., & Thomas, D. S. (1945). The Future Population of Europe and the Soviet Union: Population Projections. Journal of the American Statistical Association, 230(May), 73–76.
Petrova, K. (2019). Quasi-Bayesian Estimation of Time-Varying Volatility in DSGE Models. Journal of Time Series Analysis, 40(1), 151–157. https://doi.org/10.1111/jtsa.12290
Raman, R. K., Sathianandan, T. V., Sharma, A. P., & Mohanty, B. P. (2017). Modelling and Forecasting Marine Fish Production in Odisha Using Seasonal ARIMA Model. National Academy Science Letters, 40, 393– 397. https://doi.org/10.1007/s40009-017-0581-2
Rayer, S. (2007). Population forecast accuracy: Does the choice of summary measure of error matter? Population Research and Policy Review, 26(2), 163–184. https://doi.org/10.1007/s11113-007-9030-0
Rayer, S., & Smith, S. K. (2014). Population Projections by Age for Florida and its Counties: Assessing Accuracy and the Impact of Adjustments. Population Research and Policy Review, 33(5), 747–770. https://doi.org/10.1007/s11113-014-9325-x
Ross, E. B. (1999). The Malthus Factor : Population, Poverty and Politics in Capitalist Development. Population and Development Review, 25(2), 387–388.
Satterthwaite, D. (2004). The scale of urban change worldwide 1950-2000 and its underpinnings. Iied, 50.
Shobande, A. O. (2018). Population Crises in the Age of Slow Economic Growth : Lesson From the Asian Tigers. Journal of Social Studies, Department of Economics, NAU, 15(1), 57–75.
Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64. https://doi.org/10.1016/j.jeconom.2005.07.009
Smith, S. K., & Sincich, T. (1988). Stability Over Time in the Distribution of Population Forecast Errors. Demography, 25(3), 461–474. https://doi.org/10.2307/2061544
Tayman, J., & Swanson, D. A. (1999). On The Validity of MAPE as a Measure of Population Forecast Accuracy. Population Research and Policy Review, 18(4), 299–322. https://doi.org/10.1023/A:1006166418051
Torri, T., & Vaupel, J. W. (2012). Forecasting life expectancy in an international context. International Journal of Forecasting, 28(2), 519–531. https://doi.org/10.1016/j.ijforecast.2011.01.009
UN. (2017). World Population Prospects: Key Findings and Advance Tables. Department of Economics and Social Affairs.
World Bank. (2017). World Development Indicators, 2017.
World Bank. (2018). World Development Indicators, 2018.
Xu, X. (2019). Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning. Journal of Forecasting, 39(2). https://doi.org/10.1002/for.2599
Zhang, L., et. al. (2018). Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model. Ecological Indicators, 95(Part 1), 702–710. https://doi.org/10.1016/j.ecolind.2018.08.032
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Olatunji Abdul Shobande et al., published by Sciendo
This work is licensed under a Creative Commons Attribution 4.0 International License.