The Role of Socio-economic and Female Indicators on Child Mortality Rate in Bangladesh: A Time Series Analysis

2021 ◽  
pp. 003022282199361
Author(s):  
Mohammad Mafizur Rahman ◽  
Khosrul Alam
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
Time Series Analysis ◽  
Granger Causality ◽  
Child Mortality ◽  
Series Analysis ◽  
Life Expectancy At Birth ◽  
Augmented Dickey Fuller ◽  
Child Mortality Rate

This study aims to identify the role of socio-economic and female indicators on child mortality in Bangladesh from the data of 1975 – 2019. A number of econometric techniques of time series analysis like Augmented Dickey-Fuller, Autoregressive Distributive Lag bounds and pair-wise Granger causality tests have been applied to ascertain the desired outcomes. The Augmented Dickey-Fuller test has confirmed that neither series is integrated at level two and the Autoregressive Distributive Lag bounds testing approach has shown the cointegration and short-run and long-run relationship between the variables. Total fertility rate and urbanization have a positive effect, and female education, female life expectancy at birth, and economic growth rate have a negative effect on the child mortality rate. The pair-wise Granger causality test has shown the unidirectional and bidirectional causal relationship among the studied variables. All the outcomes are theoretically consistent and the policy recommendations are made based on findings.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

scholarly journals Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tanja Charles ◽  
Matthias Eckardt ◽  
Basel Karo ◽  
Walter Haas ◽  
Stefan Kröger
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Disease Onset ◽  
Age Groups ◽  
Age Group ◽  
Pulmonary Tb ◽  
Series Analysis ◽  
Disease Site ◽  
Extra Pulmonary

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.

scholarly journals Did the UN convention on the rights of the child reduce child mortality around the world? An interrupted time series analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Christopher A. Tait ◽  
Abtin Parnia ◽  
Nishan Zewge-Abubaker ◽  
Wendy H. Wong ◽  
Heather Smith-Cannoy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Child Mortality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Rights Of The Child ◽  
Reduce Child Mortality ◽  
The World

scholarly journals A comparative time series analysis of crude mortality rate in the BRICS countries

2021 ◽  
Vol 2 (2) ◽  
pp. 17-32
Author(s):  
Olawale Awe ◽  
O.C. Ayeni ◽  
G.P. Sanusi ◽  
L.O. Oderinde
Keyword(s):  
Time Series ◽  
Mortality Rate ◽  
Time Series Analysis ◽  
Death Rate ◽  
Structural Breaks ◽  
Policy Implications ◽  
P Value ◽  
Crude Death Rate ◽  
Series Analysis ◽  
Brics Countries

Proper research and analysis of mortality dynamics is essential to provide reliable economic information about any country. This paper deals with the historical comparative time series analysis of the mortality rate dynamics in the BRICS countries to determine their economic performances over the years. This article presents stochastic models based on autoregressive integrated moving average (ARIMA (p, d, q)) models of various orders with a view to identifying the optimal and comparative model for the crude death rate (CDR) in the BRICS countries. The ARIMA (p, d, q) models were formulated for the crude death rates in the BRICS countries and the overall annual crude death rate for the period 1960–2018. The optimal choice of ARIMA models of order p and q was selected for each of the series. The results indicate that the ARIMA (2, 2, 0) model was the optimal model for predicting mortality dynamics in the overall BRICS data. In addition, there was a significant decrease in trends (p-value < 2.22e-16) during the study period from 1960 to 2018. In addition, the crude death rate’s data for the BRICS countries proved to be mostly non-linear, non-seasonal and without structural breaks. Finally, the findings of this study were discussed and recognized as having relevant policy implications for forecasting, insurance planning, as well as for disaster or risk reduction in the context of unprecedented global happenings in the post-pandemic era.

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