[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.x10.1111/j.1751-5823.2004.tb00224.x]Search in Google Scholar
[Alho, J. M., & Spencer, B. D. (1985). Uncertain population forecasting. Journal of the American Statistical Association, 80(390), 306–314. https://doi.org/10.1080/01621459.1985.1047811310.1080/01621459.1985.10478113]Search in Google Scholar
[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/sym1102024010.3390/sym11020240]Search in Google Scholar
[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.1243010.1111/jtsa.12430]Search in Google Scholar
[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.1240910.1111/jtsa.12409]Search in Google Scholar
[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;210.2990/1471-5457(2004)23[75:TRMAEO]2.0.CO;2]Search in Google Scholar
[Bartholomew, D. J. (1971). Review Reviewed Work: Time Series Analysis Forecasting and Control. Operational Research Quarterly, 22(2), 143–144. https://doi.org/10.2307/300825510.2307/3008255]Search in Google Scholar
[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.1227910.1111/jtsa.12279]Search in Google Scholar
[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.254410.1002/for.2544]Search in Google Scholar
[Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-DAy, San Francisco, 199–201.]Search in Google Scholar
[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]Search in Google Scholar
[Brooks, C. (2019). Introductory Econometrics for Finance. Cambridge Univeristy Press: USA.10.1017/9781108524872]Search in Google Scholar
[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.x10.1111/j.1745-493X.1996.tb00225.x]Search in Google Scholar
[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.2010.06.00310.1016/j.ijforecast.2010.06.003]Search in Google Scholar
[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.2010.06.01110.1016/j.tourman.2010.06.011]Search in Google Scholar
[Day, A. (2002). The Prospects of Cosmopolitan World Order. Global Social Policy, 2(200212), 295–318.10.1177/14680181020020030401]Search in Google Scholar
[Dumont, G.-F. (2018). Urban demographic transition. Urban Development Issues, 56(4), 13–25. https://doi.org/10.2478/udi-2018-000910.2478/udi-2018-0009]Search in Google Scholar
[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/s41467-019-09833-310.1038/s41467-019-09833-3]Search in Google Scholar
[Galavi, V., & Brinkgreve, R. (2014). Finite element modelling of geotechnical structures subjected to moving loads. Numerical Methods in Geotechnical Engineering, (June), 235–240.10.1201/b17017-44]Search in Google Scholar
[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.259810.1002/for.2598]Search in Google Scholar
[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.1240810.1111/jtsa.12408]Search in Google Scholar
[Goto, Y., & Taniguchi, M. (2019). Robustness of Zero Crossing Estimator. Journal of Time Series Analysis, 40(5). https://doi.org/10.1111/jtsa.1246310.1111/jtsa.12463]Search in Google Scholar
[Hill, R. C., Griffiths, W. E., & Lim, G. C. (2011). Prinicples of Econometrics. John Wilay & Sons.]Search in Google Scholar
[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/10.1080/09672567.2012.65480510.1080/09672567.2012.654805]Search in Google Scholar
[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/10.1177/109442811666803510.1177/1094428116668035]Search in Google Scholar
[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.v39i3.1535110.3402/tellusb.v39i3.15351]Search in Google Scholar
[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-z10.1007/s12546-008-9004-z]Search in Google Scholar
[Marshall, V. M., et. al. (2017). Social Well-Being in Northern Ireland: A Longitudinal Study 1958-1998. Biological Conservation, 44(0), 1–12.]Search in Google Scholar
[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.]Search in Google Scholar
[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.1229010.1111/jtsa.12290]Search in Google Scholar
[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/s40009-017-0581-210.1007/s40009-017-0581-2]Search in Google Scholar
[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/s11113-007-9030-010.1007/s11113-007-9030-0]Search in Google Scholar
[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-x10.1007/s11113-014-9325-x]Search in Google Scholar
[Ross, E. B. (1999). The Malthus Factor : Population, Poverty and Politics in Capitalist Development. Population and Development Review, 25(2), 387–388.]Search in Google Scholar
[Satterthwaite, D. (2004). The scale of urban change worldwide 1950-2000 and its underpinnings. Iied, 50.]Search in Google Scholar
[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.]Search in Google Scholar
[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.2005.07.00910.1016/j.jeconom.2005.07.009]Search in Google Scholar
[Smith, S. K., & Sincich, T. (1988). Stability Over Time in the Distribution of Population Forecast Errors. Demography, 25(3), 461–474. https://doi.org/10.2307/206154410.2307/2061544]Search in Google Scholar
[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:100616641805110.1023/A:1006166418051]Search in Google Scholar
[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.2011.01.00910.1016/j.ijforecast.2011.01.009]Search in Google Scholar
[UN. (2017). World Population Prospects: Key Findings and Advance Tables. Department of Economics and Social Affairs.]Search in Google Scholar
[World Bank. (2017). World Development Indicators, 2017.]Search in Google Scholar
[World Bank. (2018). World Development Indicators, 2018.]Search in Google Scholar
[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.259910.1002/for.2599]Search in Google Scholar
[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.2018.08.03210.1016/j.ecolind.2018.08.032]Search in Google Scholar