Time Series Analysis of Demographic Parameters in Bangladesh
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Abstract
Demographic parameters focus on the overall health status of a country. These are necessary indicators for analyzing the health status of the sustained population in Bangladesh. Econometric models fitted on six important demographic parameters separately, three of six parameters for mortality measures and the remaining for fertility measures of Bangladesh. Autoregressive models developed on the time series data of demographic parameters such as life expectancies at birth for male and female populations, crude death rates, crude birth rates, gross reproduction rates, and net reproduction rates by year from 1980 to 2015 collected from Statistical Yearbook of Bangladesh published by the Bangladesh Bureau of Statistics. Mortality and fertility measures were predicted up to 2030 using the fitted models. The crude death rates, crude birth rates, gross reproduction rates, and net reproduction rates decreased by years from 2016 to 2030, while life expectancies at birth for the male and female populations were increasing. Government and non-government organizations and policymakers can make several decisions for more development of the sectors such as health, education, planning of food supply, and housing. Government and insurance companies in Bangladesh can also utilize the results of life expectancies at birth for the male and female population in setting the retirement age of government employees, fixing the minimum age of old age allowance, and innovating age-related rules of life insurance companies.
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References
• Aksoy, Y., Basso, H. S., Smith, R. P., & Grasl, T. (2019). Demographic structure and macroeconomic trends. American Economic Journal: Macroeconomics, 11(1), 193–222. http://dx.doi.org/10.1257/mac.20170114
• Balbo, N., Billari, F. C., & Mills, M. (2013). Fertility in advanced societies: A review of research. European Journal of Population, 29, 1–38. http://dx.doi.org/10.1007/s10680-012-9277-y
• Bangladesh Bureau of Statistics. (1989–2012). Statistical Yearbook of Bangladesh (Editions 1988–2011). Government of the People’s Republic of Bangladesh.
• Bangladesh Bureau of Statistics. (2013–2023). Statistical Yearbook of Bangladesh (Editions 2012–2021). Government of the People’s Republic of Bangladesh. https://bbs.gov.bd/site/page/29855dc1-f2b4-4dc0-9073-f692361112da/Statistical-Yearbook
• Beg, A. B. M. R. A., & Islam, M. R. (2016). Modeling and forecasting population growth of Bangladesh. American Journal of Mathematics and Statistics, 6(4), 190–195. http://article.sapub.org/10.5923.j.ajms.20160604.08.html
• Begum, S. (1990). Population birth, death and growth rate in Bangladesh: Census estimates. Bangladesh Development Studies, 18(2), 51–75. http://www.jstor.org/stable/40795379
• Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica. 47(5), 1287–1294. http://dx.doi.org/10.2307/1911963
• Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ. Computer Science, 7, Article e623. https://doi.org/10.7717/peerj-cs.623
• Christie, D., & Neill, S. P. (2022). Measuring and observing the ocean’s renewable energy resource. In J. Yan (Ed.), Comprehensive renewable energy (2nd ed., pp. 149–175). Elsevier.
• Chu, T. W., & Shirmohammadi, A. (2004). Evaluation of the SWAT model’s hydrology component in the Piedmont physiographic region of Maryland. Transactions of the ASAE, 47(4), 1057–1073. https://doi.org/10.13031/2013.16579
• Denton, F. T., Feaver, C. H., & Spencer, B. G. (2005). Time series analysis and stochastic forecasting: An econometric study of mortality and life expectancy. Journal of Population Economics, 18, 203–227. https://doi.org/10.1007/s00148-005-0229-2
• Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1302. https://doi.org/10.2307/1913829
• Goldstein, J. R., Sobotka, T., & Jasilioniene, A. (2009). The end of “lowest‐low” fertility? Population and Development Review, 35(4), 663–699. https://doi.org/10.1111/j.1728-4457.2009.00304.x
• Guets, W., & Behera, D. K. (2022). Does disability increase households’ health financial risk: evidence from the Uganda demographic and health survey. Global Health Research and Policy, 7, Article 2. https://doi.org/10.1186/s41256-021-00235-x
• Halicioglu, F. (2011). Modeling life expectancy in Turkey. Economic modelling, 28(5), 2075–2082. https://doi.org/10.1016/j.econmod.2011.05.002
• Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
• Islam, M. R., Islam, M. N., Ali, M. K., & Mondol, M. N. I. (2005). Indirect estimation and mathematical modeling of some demographic parameters of Bangladesh. Oriental Anthropologist, 5(2), 163–171. https://doi.org/10.1177/0976343020050203
• Kelley, A. C., & Schmidt, R. M. (2005). Evolution of recent economic-demographic modeling: A synthesis. Journal of Population Economics, 18, 275–300. https://doi.org/10.1007/s00148-005-0222-9
• Maddala, G. S. (1992). Introduction to econometrics (2nd Ed.). MacMillan.
• Mannan, M. I., Rahman, M. S., & Islam, M. S. (2014). Importance to construct Indigenous mortality table from crude death rate for the life insurance industry in Bangladesh. Journal of Economics and Finance, 5(6), 15–18. https://doi.org/10.9790/5933-05611518
• Matthews, S. A., & Parker, D. M. (2013). Progress in spatial demography. Demographic Research, 28, 271–312. https://doi.org/10.4054%2Fdemres.2013.28.10
• Molugaram, K., & Rao, G. S. (2017). Chapter 12 - Analysis of time series. In K. Molugaram & G. S. Rao (Eds.), Statistical Techniques for Transportation Engineering (pp. 463–489). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-811555-8.00012-X
• Muyeed, A., Siddiqi, M. N. A., & Tawabunnahar, M. (2020). Prevalence and severity of COVID-19 disease in Bangladesh: A trend analysis. Journal of Health & Biological Sciences, 8(1), 1–8. https://doi.org/10.12662/2317-3076jhbs.v8i1.3285.p1-8.2020
• Nandi, D. C., Hossain, M. F., Roy, P., & Ullah, M. S. (2023). An investigation of the relation between life expectancy and socioeconomic variables using path analysis for Sustainable Development Goals (SDG) in Bangladesh. PLOS ONE, 18(2), Article e0275431. https://doi.org/10.1371/journal.pone.0275431
• Rahman, M. M., & Alam, K. (2023). The role of socio-economic and female indicators on child mortality rate in Bangladesh: A time series analysis. Omega, 86(3), 889–912. https://doi.org/10.1177/0030222821993616
• Robinson, J. G., & Jensen, E. B. (2020). A demographic evaluation of the stability of American Community Survey (ACS) estimates for ACS test sites: 2000 to 2011. In J. Singelmann & D. Poston Jr. (Eds.), Developments in Demography in the 21st Century (The Springer Series on Demographic Methods and Population Analysis, Vol. 48, pp. 25–39). Springer, Cham. https://doi.org/10.1007/978-3-030-26492-5_3
• Rubi, M. A., Bijoy, H. I., & Bitto, A. K. (2021). Life expectancy prediction based on GDP and population size of Bangladesh using multiple linear regression and ANN model. 12th International Conference on Computing Communication and Networking Technologies (pp. 1–6). https://doi.org/10.1109/ICCCNT51525.2021.9579594
• Salvati, L., Benassi, F., Miccoli, S., Rabiei-Dastjerdi, H., & Matthews, S. A. (2020). Spatial variability of total fertility rate and crude birth rate in a low-fertility country: Patterns and trends in regional and local scale heterogeneity across Italy, 2002–2018. Applied Geography, 124, Article 102321. https://doi.org/10.1016/j.apgeog.2020.102321
• Sam, C. Y., McNown, R., & Goh, S. K. (2019). An augmented autoregressive distributed lag bounds test for cointegration. Economic Modelling, 80, 130–141. https://doi.org/10.1016/j.econmod.2018.11.001
• Senturk, I., & Ali, A. (2021). Socioeconomic determinants of gender-specific life expectancy in Turkey: A time series analysis. Sosyoekonomi, 29(49), 85–111. https://doi.org/10.17233/sosyoekonomi.2021.03.05
• Shen, T., Lazzari, E., & Canudas-Romo, V. (2023). The contribution of survival to changes in the net reproduction rate. Population Studies, 77(2), 163–178. https://doi.org/10.1080/00324728.2023.2187441
• Shubat, O., & Bagirova, A. (2022). Forecasting the length of grandparenthood with limited information resources: Evidence from Russia. Journal of Population and Social Studies, 30, 251–268. https://so03.tci-thaijo.org/index.php/jpss/article/view/257752
• Singh, J., Knapp, H. V., & Demissie, M. (2004). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004-08. Illinois State Water Survey. https://swat.tamu.edu/media/90101/singh.pdf
• United Nations. (2024). World Population Prospects 2024. https://population.un.org/wpp/
• United Nations Development Programme (UNDP). (1991). Human Development Report 1991. https://hdr.undp.org/system/files/documents/hdr1991encompletenostatspdf.pdf
• Vazquez-Amábile, G. G., & Engel, B. A. (2005). Use of SWAT to compute groundwater table depth and streamflow in the Muscatatuck River watershed. Transactions of the ASAE, 48(3), 991–1003. https://doi.org/10.13031/2013.18511
• Voss, P. R. (2007). Demography as a spatial social science. Population Research and Policy Review, 26, 457–476. https://doi.org/10.1007/s11113-007-9047-4
• Winkelmann, R., & Zimmermann, K. F. (1994). Count data models for demographic data. Mathematical Population Studies, 4(3), 205–221. https://doi.org/10.1080/08898489409525374
• Xie, Y. (2000). Demography: Past, present, and future. Journal of the American Statistical Association, 95(450), 670–673. https://doi.org/10.1080/01621459.2000.10474248
• Yang, H., Wang, S., Ren, Z., Liu, H., Tong, Y., & Wang, N. (2022). Life expectancy, air pollution, and socioeconomic factors: A multivariate time-series analysis of Beijing City, China. Social Indicators Research, 162(3), 979–994. https://doi.org/10.1007/s11205-019-02162-4