About Dataset:
My dataset represents annual population growth rate of Egypt country. Dataset is from
dataworldbank site. It can be usable for policymakers who plan public services like healthcare,
education, infrastructure, and social programs. It helps them allocate resources and deliver
services to a growing population. Also, the pace of population growth shapes economic
development strategies. Rapid growth offers potential for economic expansion through a larger
workforce and consumer market. However, slow or declining populations can pose challenges
like an aging workforce and difficulty finding qualified labor.
Annual population growth rate of Eqypt country
Using statistical analysis of time series, this paper examines Egypt's yearly population growth
from 1974 to 2022 in order to identify patterns, trends, and underlying causes influencing
demographic dynamics. We seek to offer insights necessary for comprehending Egypt's
demographic trajectory and guiding strategic decision-making processes through statistical
analysis and forecasting approaches. Dataset sourced from the DataWorldBank website.
My first step was data preparation:
1. Time Variable: I created a new variable to represent the time series, due to the Stata not
recognizing original “Year” row.
3. Timeseries Declaration: After this, I could define the dataset as a time series with yearly
observations from 1975 to 2022(including).
My second step was Stationarity Analysis:
1. Visual Inspection: I examined a graph of the original population growth rate (tsline Population)
and determined it was non-stationary (negative trend).
3. Differencing: To achieve stationarity, I applied first-order differencing to the population growth
rate variable. This created a new line named "d.Population". By looking at the graph, I declared
this variable suitable to my project.
My third step was ARIMA Model Selection:
1. Partial Autocorrelation Function (PACF): I used the PACF to identify the appropriate
autoregressive (AR) order (p). Based on the graph, I chose p = 1 as higher orders displayed
insignificant correlations.
3. Autocorrelation Function (ACF): I used the ACF to determine the moving average (MA) order
(q). While both q = 1 and q = 2 were possible choices, I opted for q = 1 due to its higher
correlation and for model simplicity(the simpler is better).
5. Differencing (d): Since we differenced the data once, at the previous step, d = 1.
I looked at the autocorrelation structure using the Partial Autocorrelation Function (PACF) to
determine the proper autoregressive (AR) order (p). I determined that p = 1 after examining the
graph because higher orders showed negligible correlations.
The moving average (MA) order (q) was then determined by using the Autocorrelation Function
(ACF). Both q = 1 and q = 2 were good choices, but I went with q = 1 because of the better
connection and to keep the model simple, emphasizing a more efficient way to model.
Since the data had already gone through one differencing step in the previous analysis, I came
to the conclusion that d = 1 for differencing (d).
My fourth step was ARIMA(1,1,1) Model Fitting and interpretation of model:
1. Constant Term Removal: The initial ARIMA(1,1,1) model included a constant term, but its
value was statistically insignificant. I removed the constant term to get significant model.
2. Significance checking: Log likelihood is equal to 79.7, it indicates wellness of model. The
more is better in this case. Also, the chi-square statistic (176.20) and its significance level
(Prob> chi2 = 0.0000) shows that coefficients are statistically significant. P-values of each of
them are significant.
3.⁠ ⁠Interpretation: Our model says that today’s first order differenced population growth
rate of Egypt is equal to 0.53 multiplied by previous differenced population growth rate
+ 0.47 multiplied by previous period error.
Implications for Decision-Making and Problem-Solving:
1. Predict future needs: By forecasting population trends, the model informs policies related to
healthcare infrastructure, education capacity, and social security programs. Anticipating
population shifts (growth or decline) allows for preparation regarding changes in age structure
and dependency ratios.
2. Plan for the workforce: The model's forecasts can guide economic planning by anticipating
the size and composition of the labor force. High growth rates might necessitate investments in
skills training and job creation, while low growth could require policies to address the needs of
an aging population.
Improvements or further steps based on the analysis outcomes:
1. Our analysis can be enhanced by incorporating factors influencing population growth, such
as birth rates, mortality rates, immigration, and emigration. This would provide a deeper
understanding of the drivers behind population changes. Also, Egypt is tourist-centric country,
and part of them stays there for a long.
2. Investigating seasonal components in Egypt's population growth data will be important
improvement. Seasonal variations in birth rates or migration patterns can be added. Techniques
like seasonal differencing or SARIMA models can capture these seasonal effects and improve
model accuracy.
Improvements or further steps based on the analysis outcomes.
Important Elements: It would be helpful to add variables that affect population growth, such as
immigration, emigration, death rates, and birth rates, to improve our analysis. These elements
may offer more in-depth understanding of the causes of population shifts. Furthermore, given
Egypt's reputation as a tourism-heavy nation, it's critical to take long-stay visitors into account
because they have the potential to significantly alter population dynamics.
Seasonality: It would be beneficial to examine seasonal differences in Egypt's population growth
statistics. The dynamics of population can be significantly impacted by seasonal trends in
migration or birth rates. Seasonal Autoregressive Integrated Moving Average models and
seasonal differencing are two methods that can help us better anticipate population growth by
capturing these seasonal influences. We would be able to produce more accurate forecasts and
gain a deeper understanding of the seasonal tendencies present in the data with this approach.
The model helps plan for the future by looking at how the population is changing. This helps
decide things like how many hospitals or schools we might need, and how to support people as
they get older.
2. It helps businesses and governments figure out how many workers they'll need in the future. If
the population is growing fast, they might invest in training programs and new jobs. If it's growing
slowly, they might focus on helping older workers.
The impact, on Decision Making and Problem Solving.
Urban Development and Infrastructure; The rapid growth of areas often leads to an increase in
population placing pressure on the citys services and infrastructure. Decision makers may need
to allocate resources towards projects like housing, water supply, transportation and sanitation to
support the expanding population. Prioritizing infrastructure investments and adapting planning
strategies based on population trends can contribute to development and improve the quality of
life for residents.
Environmental Concerns; With population growth comes challenges for the environment and
natural resources affecting biodiversity, land usage and water availability. Decision makers should
consider how population growth impacts the environment and implement policies that promote
conservation efforts, sustainable resource management and measures to address climate
change.
Enhancements or next steps following the analysis results.
Key Factors; Including factors such as immigration rates, emigration patterns, mortality rates and
birth rates in our analysis can provide a understanding of population shifts. These factors could
offer insights into the underlying reasons for changes, in population demographics.
Moreover considering Egypts known status, as a tourist destination it is important to consider the
impact of long term visitors as they could potentially affect the demographics in a notable way.
Regarding variations; It would be valuable to explore the differences in population growth trends
in Egypt based on seasons. The ebb and flow of population dynamics may be significantly
influenced by patterns related to migration or birth rates. By utilizing Seasonal Autoregressive
Integrated Moving Average models and seasonal differencing techniques we can enhance our
ability to predict population changes by capturing these fluctuations. This approach would enable
us to generate forecasts and deepen our understanding of the seasonal patterns inherent, in the
data.