Forecasting Rate of Decline in Infant Mortality in South Asia Using Random Walk Approximation
DOI:
https://doi.org/10.6000/1929-6029.2014.03.03.7Keywords:
Infant mortality, ARIMA model, random walk, MDGs, demographic forecast, unit root test.Abstract
The Millennium Development Goal 4 (MDG 4) of United Nations had set the target of reducing high rates of under-five and infant mortality (IMR) by two thirds to be reached by 2015 using 1990 as the benchmark year. By the availability of time series data on IMR from United Nations Inter-agency Group for Child Mortality Estimation (UN IGME, 2012), led by UNICEF, WHO, the World Bank and United Nations, it has become possible to track the rate of progress towards this goal. Using the UN IGME 2012 data for all the South Asian Countries, I have considered three specific issues in this article. (1) How does the South Asian Countries fair in reducing the IMR towards this MDG target? Although the time series data exhibit declining trends for all the countries in South Asia, to what extent such trends are attributed by their average annual progress trajectory over the period for which data are available? (2) Whether deterministic or stochastic trend can attribute the IMR decline in South Asian countries and what alternative time series models be used to forecast the decline in Infant Mortality? Can we find a serviceable representative model for the entire region? (3) In case, a satisfactory representative model for the entire region exists, how do we assess the forecast accuracy for this model and quantify the propagation of forecast error?
References
United Nations. The Millennium Development Goals Report 2011, The United Nations: New York; 2011.
World Bank. Attaining the Millennium Development Goals in India: Role of Public Policy & Service Delivery. Washington DC USA; 2004.
Chatterjee A, Paily VP. Achieving Millennium Development Goals 4 and 5 in India. BJOG 2011; 118(suppl. 2): 47-59. http://dx.doi.org/10.1111/j.1471-0528.2011.03112.x DOI: https://doi.org/10.1111/j.1471-0528.2011.03112.x
Sundaram K, Tendulkar S. Poverty Has Declined in the 1990s – A Resolution of the Comparability Problems in NSS Consumer Expenditure Data. Economic and Political Weekly 2003; 38: 25-31.
Sundaram K, Tendulkar S. Poverty in India in the 1990s – Revised Results for India and the 15 Major States for 1993-94. Economic and Political Weekly 2003; 38: 15-21.
UNICEF. Levels and Trends in Child Mortality Report 2012. United Nations Children’s Fund: USA; 2012.
World Bank. World Development Indicators 2011. Washington, D.C., USA, 2011.
Bajpai N. India: Towards the Millennium Development Goals, Background Paper, Human Development Report, UNDP; 2003: Available from: http://hdr.undp.org/docs/publications/ background_papers/2003/India/India_2003.pdf
Sullivan JM, Shea OR, George TB. Infant and Child Mortality. DHS Comparative Studies No. 15; Macro International Inc: Calverton Maryland 1994.
United Nations Inter-agency Group for Child Mortality Estimation. UN IGME Total under-5 Mortality Rate and Infant Mortality Rate database 2012; Available from: http://www.childmortality.org/
Alho JM, Spencer BD. Statistical Demography and Forecasting. Springer Science+ Buisness Media Inc: USA; 2005.
Bishai DM. Infant Mortality Time series are random walks with drift: Are they cointegrated with socioeconomic variables? Health Economics 1995; 4(3): 157-167. http://dx.doi.org/10.1002/hec.4730040302 DOI: https://doi.org/10.1002/hec.4730040302
Dickey DA and Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 1979; 74: 427-431 DOI: https://doi.org/10.1080/01621459.1979.10482531
MacKinnon JG. Critical values for cointegration tests, in: Engle RF, Granjer CWJ, editors. Long-Run Economic Relationships: Reading in Cointegration. Oxford University Press 1991; p. 267-76.
Montgomery DC, Jennings CL, Kulahci M. Introduction to Time Series Analysis and Forecasting. Wiley: New York; 2008.
Shumway RH, Stoffer DS. Time Series Analysis and its Applications With R Examples. Springer Science + Buisness Media: LLC; 2011. DOI: https://doi.org/10.1007/978-1-4419-7865-3
Girosi F and King G. Demographic Forecasting. 2008. Princeton University Press, New Jersey; 2008. DOI: https://doi.org/10.1515/9780691186788
Dorn H. Pitfalls in population forecasts and projections. Journal of the American Statistical Association 1950; 45: 311-334. http://dx.doi.org/10.1080/01621459.1950.10501127 DOI: https://doi.org/10.1080/01621459.1950.10501127
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Tapan Kumar Chakrabarty
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
