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Testing Fractional Persistence and Nonlinearity in Infant Mortality Rates of Asia Countries

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  • Yaya, OlaOluwa S
  • Adekoya, Oluwasegun B.
  • Babatunde, Oluwagbenga T.

Abstract

The infant mortality rates in 45 Asian countries (1960-2018), obtained from the Federal Reserve Bank of St. Louis database, are investigated using the I(d) framework, which allows for simultaneous estimation of the degree of persistence and nonlinearities in infant mortality rates as well as their growth rates. A high degree of persistence in the decreases of mortality rate is found with nonlinear evidence in the majority of the cases, confirming nonlinear dynamics of mortality rates. In the growth of mortality rates, we find ten countries (Armenia, Indonesia, Israel, Japan, Kuwait, Myanmar, Saudi Arabia, Sri Lanka, Thailand, and UAE) with evidence of mean reversion. Health management in those listed countries needs to kick start interventions that improve the survival rates of infants.

Suggested Citation

  • Yaya, OlaOluwa S & Adekoya, Oluwasegun B. & Babatunde, Oluwagbenga T., 2021. "Testing Fractional Persistence and Nonlinearity in Infant Mortality Rates of Asia Countries," MPRA Paper 109370, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:109370
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    References listed on IDEAS

    as
    1. Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214.
    2. OlaOluwa Simon Yaya & Luis Alberiko Gil-Alana, 2020. "Modelling Long-Range Dependence and Non-linearity in the Infant Mortality Rates of African Countries," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 26(3), pages 303-315, August.
    3. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2015. "Infant mortality rates: time trends and fractional integration," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 589-602, March.
    4. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    5. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    6. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
    7. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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