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The secondary data used in this study was extracted from\nADB and the World Bank Database. In this study, the under-five mortality rates over\nthe years (2003-2017) are modeled using time series analysis. This study designs to\npredict the future of Myanmar and some neighbouring countries by selecting the\nappropriate model among four commons models as Linear, Quadratic, Cubic, and\nExponential. In Myanmar, the values of coefficient in the Exponential model are\nstatistically significant and the value MSE (63.2685) for Exponential model is the\nsmallest on comparing with other models. Thus, the Exponential model was chosen as\nthe most appropriate and predictable future model in Myanmar. It found that the\nforecast value of U5MR in Myanmar was decreased little by little from 40.66 in 2018\nto 32.91 in 2021. In Thailand, the values of coefficient in four common models are\nstatistically significant and the value of MSE (6.9064) for the Cubic model is the\nsmallest on comparing with other models. Thus, the Cubic model was chosen as the\nmost appropriate and predictable future model in Thailand. It found that the forecast\nvalue of U5MR in Thailand was decreased little by little from 5.66 in 2018 to -14.51\nin 2021. In China, the values of coefficient in four common models are statistically\nsignificant and the value of MSE (0.4459) for the Cubic model is the smallest on\ncomparing with other models. Thus, the Cubic model was chosen as the most\nappropriate and predictable future model in China. It found that the forecast value of\nU5MR in China was decreased little by little from 7.47 in 2018 to 0.117 in 2021. In\nIndia, the values of coefficient in the Linear model are statistically significant and the\nvalue of MSE (3.4277) for the Linear model is the smallest on comparing with other\nmodels. Thus, the Linear model was chosen as the most appropriate and predictable\nfuture model in India. It found that the forecast value of U5MR in India was\ndecreased little by little from 36.13 in 2018 to 26.22 in 2021. In Bangladesh, the\nvalues of coefficient in Quadratic model are statistically significant and the value\nMSE (9.6080) for the Quadratic model is the smallest on comparing with other\nmodels. Thus, the Quadratic model was chosen as the most appropriate and\npredictable future model in Bangladesh. It found that the forecast value of U5MR in\nBangladesh was decreased little by little from 30.4 in 2018 to 26.43 in 2021. Thus, the\nforecast values of under-five mortality rates (2018 to 2021) were decreasing in\nMyanmar and neighbouring countries. Forecasting is very important in future\ndecisions making. 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  1. Yangon University of Economics
  2. Master of Applied Statistics (MAS)

Modelling and Forecasting of the Under-Five Mortality Rates in Myanmar and Some Neighbouring Countries

https://meral.edu.mm/records/8681
https://meral.edu.mm/records/8681
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Title
Title Modelling and Forecasting of the Under-Five Mortality Rates in Myanmar and Some Neighbouring Countries
Language en
Publication date 2020-03-01
Authors
Tin Mar Su
Description
The under-five mortality rate is a key indicator of the state of public health of
a society. Under-five mortality statistics is not only important indicators of
demographic situation but also social and health conditions of Myanmar and some
neighbouring countries. The secondary data used in this study was extracted from
ADB and the World Bank Database. In this study, the under-five mortality rates over
the years (2003-2017) are modeled using time series analysis. This study designs to
predict the future of Myanmar and some neighbouring countries by selecting the
appropriate model among four commons models as Linear, Quadratic, Cubic, and
Exponential. In Myanmar, the values of coefficient in the Exponential model are
statistically significant and the value MSE (63.2685) for Exponential model is the
smallest on comparing with other models. Thus, the Exponential model was chosen as
the most appropriate and predictable future model in Myanmar. It found that the
forecast value of U5MR in Myanmar was decreased little by little from 40.66 in 2018
to 32.91 in 2021. In Thailand, the values of coefficient in four common models are
statistically significant and the value of MSE (6.9064) for the Cubic model is the
smallest on comparing with other models. Thus, the Cubic model was chosen as the
most appropriate and predictable future model in Thailand. It found that the forecast
value of U5MR in Thailand was decreased little by little from 5.66 in 2018 to -14.51
in 2021. In China, the values of coefficient in four common models are statistically
significant and the value of MSE (0.4459) for the Cubic model is the smallest on
comparing with other models. Thus, the Cubic model was chosen as the most
appropriate and predictable future model in China. It found that the forecast value of
U5MR in China was decreased little by little from 7.47 in 2018 to 0.117 in 2021. In
India, the values of coefficient in the Linear model are statistically significant and the
value of MSE (3.4277) for the Linear model is the smallest on comparing with other
models. Thus, the Linear model was chosen as the most appropriate and predictable
future model in India. It found that the forecast value of U5MR in India was
decreased little by little from 36.13 in 2018 to 26.22 in 2021. In Bangladesh, the
values of coefficient in Quadratic model are statistically significant and the value
MSE (9.6080) for the Quadratic model is the smallest on comparing with other
models. Thus, the Quadratic model was chosen as the most appropriate and
predictable future model in Bangladesh. It found that the forecast value of U5MR in
Bangladesh was decreased little by little from 30.4 in 2018 to 26.43 in 2021. Thus, the
forecast values of under-five mortality rates (2018 to 2021) were decreasing in
Myanmar and neighbouring countries. Forecasting is very important in future
decisions making. The forecast based on the appropriate models were also validated
in this thesis to support future decision making for planning purpose.
Thesis/dissertations
Yangon University of Economics
Daw Thin Marlar Oo
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