Authors :
Aritra Sen; Shalmoli Dutta
Volume/Issue :
Volume 5 - 2020, Issue 8 - August
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3gZ5czO
DOI :
10.38124/IJISRT20AUG755
Abstract :
Mortality is a continuous force of attrition,
tending to reduce the population, a prime negative force
in the balance of vital processes (Bhasin and Nag, 2004).
Sample Registration System (SRS) serves as the only
source of annual data on vital events on a full scale from
1969-70 in India. Few studies have examined the trends
and patterns of mortality across time and regions in
India (Preston and Bhat, 1984). The Under 5 Mortality
Rates (U5MR) can be seen to decrease by more than
half from 1970 to 2017 but in contrast little is known
about the mortality patterns of the older children (5-9)
and young adolescents (10-14), and not many studies
have been done on their changing trends (Masquelier et
al., 2018).
Using the annual data for the 5-14 age, the trend of
decline in the mortality patterns is studied from 1970 to
2013. The linear trend in the time series plot suggests
analysis using time series models AR(p), MA(q),
ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random
Walk with drift models to get the best fit to the trend of
the data. The order of the time series models have been
calculated by studying the ACF, PACF plots and the
coefficients have been derived using the Yule-Walker
equation matrix. An in-sample forecast of the years
2014-17 are taken. The Mean Squared Error (MSE) and
the Mean Absolute Percentage Error (MAPE) as a
measure of accuracy is used to determine the best fit
model. ARIMA(3,1,1) produced lower values making it
the best-fit model.
Out-of-sample forecasting was done for 2018-2025.
The forecast value shows that at the current trend,
India would have 0.03 deaths per 1000 population in the
5-14 age group in 2025 showing that the government’s
policies and health care interventions towards
realization of the MDG4 goal is working positively.
Keywords :
ARIMA; yule-walker; SRS; MAPE; MSE
Mortality is a continuous force of attrition,
tending to reduce the population, a prime negative force
in the balance of vital processes (Bhasin and Nag, 2004).
Sample Registration System (SRS) serves as the only
source of annual data on vital events on a full scale from
1969-70 in India. Few studies have examined the trends
and patterns of mortality across time and regions in
India (Preston and Bhat, 1984). The Under 5 Mortality
Rates (U5MR) can be seen to decrease by more than
half from 1970 to 2017 but in contrast little is known
about the mortality patterns of the older children (5-9)
and young adolescents (10-14), and not many studies
have been done on their changing trends (Masquelier et
al., 2018).
Using the annual data for the 5-14 age, the trend of
decline in the mortality patterns is studied from 1970 to
2013. The linear trend in the time series plot suggests
analysis using time series models AR(p), MA(q),
ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random
Walk with drift models to get the best fit to the trend of
the data. The order of the time series models have been
calculated by studying the ACF, PACF plots and the
coefficients have been derived using the Yule-Walker
equation matrix. An in-sample forecast of the years
2014-17 are taken. The Mean Squared Error (MSE) and
the Mean Absolute Percentage Error (MAPE) as a
measure of accuracy is used to determine the best fit
model. ARIMA(3,1,1) produced lower values making it
the best-fit model.
Out-of-sample forecasting was done for 2018-2025.
The forecast value shows that at the current trend,
India would have 0.03 deaths per 1000 population in the
5-14 age group in 2025 showing that the government’s
policies and health care interventions towards
realization of the MDG4 goal is working positively.
Keywords :
ARIMA; yule-walker; SRS; MAPE; MSE
