Journal of the Royal Statistical Society Nigeria Group (JRSS-NIG Group) ISSN NUMBER: 1116-249X

LIFE EXPECTANCY - POVERTY DYNAMICS: AN AUTOREGRESSIVE DISTRIBUTED- LAG (ARDL) APPROACH

2024-12-02T13:30:06+01:00

This study examines the relationship between males' life expectancy at birth (LEM) and poverty
(POV), particularly the unilateral relationship from the predictor POV to the dependent LEM. To
enhance the robustness of parameter estimations, the Naira-Dollar exchange rate (EXR) was
included as a control variable alongside POV. Yearly time series data collected on LEM, POV,
and EXR spanning 1981 to 2023 was used in our study. Moreover, to determine the possible
presence of short and long-run relationships among these three series, we used the
Autoregressive Distributed-Lag (ARDL) model for examining these series. Basic pre-test results
of ARDL such as first difference stationary conditions (I(1)s) and lag selection criteria jointly
selected the ARDL(1, 3, 2) model as the optimal model for examining the series. Moreover,
diagnostic checking on the model’s residual showed that it is non-autocorrelated and non-
spurious (R 2 (0.999367=) < Durbin-Watson (=2.404865)). The findings established that changes
in EXR and POV have an immediate lag on life expectancy, with EXR fluctuations having
complex short and long-term effects and POV having significant delayed negative effects.
Further findings revealed that the Error Correction Term (ECT) has the correct sign (-0.061242)
which indicates a 6.1% adjustment rate back to the long-run equilibrium per period.

Modelling Monthly and Annual variations in precipitation across Geopolitical Zones in Nigeria.

2024-12-03T09:13:38+01:00

Rainfall is a major component of the water cycle, and it is responsible for depositing most of
the fresh water on the earth. It provides suitable conditions for many types of ecosystems, as
well as water for hydro-electric power plant and crop irrigation. Climate change and its
potential impacts have become pressing global concerns, making a thorough understanding of
rainfall patterns in specific regions crucial for sustainable development and disaster
management. This research presents an analysis of rainfall across Nigeria by investigating the
variations in rainfall distribution and intensity over different regions and time periods. This
work utilizes historical meteorological records from the NASA Access Data Viewer platform.
Spatial and trend analysis techniques are employed to identify areas with significant
differences in precipitation amounts and variations. Markovian modelling was also
introduced to model the rainfall dynamics. This enabled the forecast of probabilities of
occurrence. Temporal trends are examined to detect any long-term patterns or shifts in the
occurrence of rainfall events. This study showed that rainfall variability over time follows a
trend within a certain arbitrary boundary with many states now witnessing greater annual
rainfall, but with high variability within the rainy months of the year. The change in the
pattern of rainfall has led to widespread extreme events and a reduction in the length of the
growing season across the country. Such prolonged variability in rainfall may have a
significant effect on the groundwater resources and the hydrology of Nigeria. Therefore,
farmers should endeavour to adopt crops that are drought resistant and early maturing
especially in the Northern region of the country. Other adaptive measures for climate change
include users adjusting their farming calendars for irrigated agriculture according to the
changing rainfall period. It is believed that these recommendations among others could help
avert the impending food insecurity in Nigeria, particularly as the population has been
predicted to double by the year 2050.

COMPARATIVE ANALYSES OF DISTRIBUTIONS IN ASSYMETRY GARCH MODELLING: A STUDY OF NGN/USD EXCHANGE RATE

2024-12-03T09:21:15+01:00

In an era of globalization characterized by flexible exchange rate systems, including Nigeria,
the examination of foreign exchange rate volatility has become critically significant in recent
decades, attracting the interest of both scholars and policymakers. Examining the dynamic
variability of exchange rate series with distributional assumptions is highly significant. This
paper examines the volatility of the Naira/US dollar exchange rate utilizing the EGARCH (1,
1) model from the GARCH family, with particular attention to generalized t, skewed student
t, and skewed normal distributions. Data on the secondary Naira/Dollar exchange rate was
obtained from the Central Bank of Nigeria's website, covering the period from January 2003
to April 2023. Monthly exchange rate returns were utilized to estimate the GARCH
parameters employing the previously described distributions. The findings demonstrated that
the skew student t (ST) distribution had superior predictive capability for N/$ exchange rate
volatility, as evidenced by its elevated log-likelihood, reduced AIC, and diminished BIC
within the chosen EGARCH (1, 1) model family. Furthermore, the results from the forecast
evaluation revealed the existence of generated conditional variance, suggesting that the
variance reverts to a long-term mean. The EGARCH model provided significant insights into
volatility dynamics; however, it is concluded that the selection of distribution is crucial for
improving its performance, with the skew Student's t distribution, due to its flexibility and
adaptability to varying market conditions, identified as the most effective estimator in this
study

GARIMA FIRST-ORDER AUTOREGRESSIVE PROCESS: PROPERTIES, ESTIMATION METHODS AND APPLICATION

2024-12-03T09:39:25+01:00

A new stationary autoregressive process with the Garima marginal distribution is introduced in this paper. Properties of the model such as the distribution of the corresponding error term, conditional moments, time irreversibility, autocorrelation function, spectral density and run probabilities are extensively studied. A simulation study is carried out to compare the performance of the Yule-Walker, conditional least squares and Gaussian estimation procedures in estimating the parameters of the new model. The simulation results indicate that the Gaussian estimation technique is the best among the three methods. The fit of the model to German bilateral real exchange rate data is compared with fits of three existing AR(1) models namely, Gaussian, Exponential and Lindley AR(1) models using Akaike information criterion (AIC) and Bayesian information criterion (BIC). The proposed model is found to be the best for modeling the data among the fitted models since it corresponds to the smallest value of the AIC and BIC.

MODELING NIGERIAN STOCK PRICE VOLATILITY USING EGARCH-X MODEL WITH DIFFERENT INNOVATIONS

2024-12-03T10:25:35+01:00

This study explores the modelling performance of EGARCH-X using the skewed student’s t,
normal, and student’s t innovations. The aim of the study was to determine the innovation that
best captures the asymmetry and kurtosis exhibited by the returns on financial data. The
descriptive statistics revealed that the distributions of returns on the stock prices were skewed
and leptokurtic. The unit root test was carried out using the Augmented Dickey-Fuller (ADF)
test. The result of the unit root test reveals that the returns on the series were stationary. The
ARCH LM-test detected the presence of ARCH effects. The mean equation was estimated, and
the EGARCH-X (1,1) model was fitted to the data, incorporating three exogenous variables
(daily opening price, daily low price, and daily high price). The goodness of fit of the models
was tested using Akaike Information Criterion, Bayesian Information Criterion, and Log-
Likelihood. The models' performance, based on Akaike Information Criterion (AIC), Bayesian
Information Criterion (BIC), and Log-Likelihood, revealed that EGARCH-X (1,1) with skewed
student's t innovation performs better than EGARCH-X (1,1) with normal and student’s t
innovations. The findings of the study further revealed that volatility persists longer in the
models with Student’s t innovations, suggesting a slower mean-reverting process pertinent for an
appropriate forecast of the financial market.

ENERGY-GROWTH DYNAMICS IN THE PRESENCE OF INCOME OF 5 OIL- EXPORTING AFRICAN COUNTRIES: A TIME SERIES APPROACH

2024-12-06T15:05:00+01:00

This study examines the relationship between economic growth and energy consumption in five African oil-exporting countries—Nigeria, Congo, Egypt, Algeria, and Gabon—from 1980 to 2021. Using Vector Autoregression (VAR), Vector Error Correction Model (VECM), and Granger causality tests, distinct causal patterns are identified. In Algeria, GDP and energy consumption show no significant interaction. In Egypt, energy consumption drives GDP growth.
Gabon demonstrates strong short- and long-term causality between the variables. In Nigeria, energy consumption influences GDP in the short term, while in Congo, GDP has a significant long-term effect on energy consumption. These findings highlight varying policy implications for energy and economic planning in each country.

IMPACT OF COVID-19 ON THE ACADEMIC PERFORMANCE OF SCIENCE STUDENTS USING BAYESIAN MODEL

2024-12-06T15:08:29+01:00

Considering the emergence of the global COVID-19 pandemic, teaching and learning activities were interrupted for almost a year across the Senior Secondary Schools in Nigeria. This research work seeks to investigate and evaluate the degree of impact of COVID-19 on the academic performance of Science students from four (4) Science subjects; Mathematics, Physics, Chemistry and Biology using a Bayesian Hierarchical Linear Mixed Effects Model (BHLMEM) fitted to cross-sectional data. The Bayesian Model is designed for this application which allows student-specific error variances to vary across the Science subjects. The data collected was analyzed using Residual Maximum Likelihood in R package. It was clearly evident that COVID-19 had a huge impact on the academic performance of Science students in Secondary schools. Some recommendations were suggested to the educators that it is important for teachers to seamlessly integrate and infuse themselves into the cutting-edge paradigm shift of the 21st century known as digital mode of teaching, and they should train and guide the
learners until they acclimatize to the ecosystem of online teaching.

Modified Two-Stage Cluster Sampling Estimators for Finite Population Total

2024-12-06T15:10:48+01:00

This work proposes modified versions of the conventional estimators for finite population total under two stage cluster sampling scheme by combining the efficiency gain in Probability Proportional to Size and stratification using both Equal and Unequal probability of selection methods. The population is first stratified into strata and independent samples of equal size is selected from each stratum using probability Proportional to size (PPS) without
replacement in the first stage. In the second stage, hm and him units are selected for equal and unequal probability methods respectively using Simple Random Sampling without replacement (SRSWOR). The variances of the suggested estimators are expressed mathematically.The empirical comparison of the variances, standard errors and the coefficient of variations were used in obtaining the most efficient estimator. It was established that, the proposed estimators were better than the conventional ones. The one that uses unequal probability of selection method performs better amongst the proposed ones and therefore recommended.

STATISTICAL ANALYSIS OF CRIME PATTERNS IN KATSINA METROPOLIS

2024-12-06T15:12:52+01:00

On the Comparison of VECH and BEKK in Modeling of Oil Prices, Stock Exchange, Exchange and Inflation Rates Volatility in Nigeria

2024-12-06T15:16:45+01:00

A crucial component of financial time series is the modeling of volatility and co-volatility. The variances and covariances among financial data are modeled by multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models. The generalized autoregressive conditional heteroscedasticity (GARCH) model has been used to describe the volatility of a variety of univariate time series data, but there has not been much research done on
using multivariate GARCH models to model multivariate time series data. Thus, this study aimed at comparing the performance of vector error conditional heteroscedasticity (VECH) and Baba Engle, Kraft, and Kroner (BEKK) MGARCH in modeling of oil prices, stock exchange, inflation and exchange rates volatility in Nigeria. The data for the study were collected from Central Bank of Nigeria Website and World Bank Data base. The data collected were analyzed using Augmented Dickey Fuller (ADF) test, diagonal VECH and diagonal BEKK. The results of the analysis revealed that diagonal BEKK performed better than the diagonal VECH in terms of model selection criterion. Based on the conditional-covariance results, it was concluded that the volatility spillover effects were strong and significant for all the variables except for the shocks of the returns of inflation rate and persistence shocks for the returns of exchange rate. Also, the
magnitude of the estimate is not homogeneous across the variables but remains within a relatively tight range. The study recommended that further research should consider comparing diagonal BEKK with other MGARCH models.

FORECASTING THE OCCURRENCE OF DRY SPELL DURING THE GROWING SEASON IN NIGERIA: A REGRESSION MODEL APPROACH

2024-12-06T15:21:04+01:00

This study examines the relationship between meteorological factors and dry spell occurrences during growing seasons in Nigeria. Using historical data from 2018 to 2023, we analyzed six geopolitical regions, each represented by one state. We collected and analyzed historical meteorological data (rainfall, temperature, and humidity) from the six states. Correlation analysis identified significant relationships between variables. We derived a Temperature Range variable from maximum and minimum temperatures and employed Ordinary Least Squares (OLS) regression and machine learning regression models to predict rainfall patterns. Significant correlations exist between rainfall, temperature, and humidity. Temperature Range and Relative Humidity effectively predict future rainfall patterns. Regional temperature variations were observed, with high temperatures prevailing in northern regions and low temperatures
characterizing southern regions. A strong negative correlation exists between Temperature Range and annual rainfall. Furthermore, regions with lower temperature ranges exhibit higher humidity, leading to increased rainfall and reduced dry spells. Notably, machine learning regression models
better OLS regression due to relaxed normality assumptions. This study enhances understanding of dry spell predictions in Nigeria's growing seasons, providing valuable insights for agricultural planning and climate resilience strategies.

EXPONENTIATED GENERALIZED BURR XII DISTRIBUTION: PROPERTIES AND APPLICATIONS

2024-12-06T15:51:02+01:00

This study introduces a new four-parameter distribution, the Exponentiated Generalized Burr XII (EGBXII) distribution. The model was developed by combining the classical Burr XII distribution with the Exponentiated Generalized family, offering enhanced flexibility. Its probability density function (pdf) exhibits desirable features, including unimodal and inverted bathtub shapes. The hazard rate function can represent both increasing and decreasing patterns, making it versatile for modeling diverse real-world phenomena. Key properties of the distribution, such as moments, the moment-generating function, skewness, and kurtosis, are derived. The parameters of the distribution were estimated using the maximum likelihood method. A simulation study was conducted to evaluate the behavior of these estimated parameters. Finally, the proposed model was applied to two real-world datasets, where it demonstrated superior performance in terms of efficiency and consistency compared to other existing models, as evaluated using comparative criteria, including Akaike information Criterion (AIC), Bayesian information criterion (BIC), Hannan Quinn information criterion (HQIC), Corrected Akaike Information Criterion (CAIC), and the Kolmogorov-Smirnov (KS) test.

DOSPREVENT: A PROACTIVE DENIAL OF SERVICE (DOS) ATTACK PREVENTION TOOL AGAINST CRITICAL INFORMATION INFRASTRUCTURE

2024-12-06T15:53:04+01:00

Denial of service (DoS) attack is generally a malware attack to overwhelm a computer system, websites or network with unwarranted excessive traffic hence making it inaccessible to genuine users. Denial of service (DoS) attacks pose a significant threat to critical information infrastructure (CII) networks as they can disrupt essential services and potentially cause widespread damage to the infrastructure. This attack aimed at overcoming the availability of the information infrastructure’s network with a huge number of traffic hence making it unavailable for business activities. This work proposes a preventive approach to tackle the issue of DoS attacks on critical information infrastructures using a packet filtering approach. The algorithm attempts to filter incoming packets and get their time-to-life value which was then used to determine the hop-count computation detecting DoS packets from legitimate packets. The hop-count gives accurate detection with 0.05% false positive with an accuracy of 97%. The system monitors the packets coming into the information infrastructure’s network and proactively detect DoS attack before damaging the system. The proposed system is a preventive measure for CII against DoS attack.

FORECASTING NIGERIA'S ECONOMIC DEVELOPMENT: EMPIRICAL ANALYSIS OF AGRICULTURE, INDUSTRY AND SERVICES USING MINITAB 20.2.0 SOFTWARE (FROM 1981 TO COVID-19)

2024-12-06T15:56:16+01:00

This paper attempt to investigate on Nigeria’s economic growth. In the pursuit to understand the contribution and effect of GDP on its components namely; Agriculture, Industry and Services by considering there growth rate from their respective exponential growth curves models. While Yearly Data were sources from CBN website www.cbn.gov.ng span from 1981 to 2019 of real sector statistics. Minitab 20.2.0 software was used to accomplish the task of the key economy
sectors. A, such Beverton-Holt Economic Recruitment model was applied in other to check the economic growth rate over the period. The model shows that GDP, Agriculture, Services follows unlimited growth Pattern while, industry follows logistics growth Pattern through the various sector trend respectively. Further findings shows that exponential standard measures of forecast accuracy with, 26 MAPE, 2727 MAD and 2181586115 MSD means services sector and industry
sector value had the best smaller model fit. The regression estimates on Agriculture 65.9%, R- sq(adj) of 99.30%, Industry 98.1% R-sq(adj) 97.48% and Services 83.3%, R-sq(adj) of 99.31% measures the goodness of fit and has a positive coefficient determination statistically improved also meaning that all the regression model is approximately significant for this key sectors respectively, the variance inflation factor (VIF) measures the effect of collinearity among the
component in the regression model. VIF is 1/tolerance in addition, the tolerance value is exactly 1, and this further substantiates the absence of multicollinearity in the study. Based on these key findings, the study therefore conclude and recommends that service sector, industrial sector and agricultural sector on the overall GDP respectively played a significant role as it affect the economic development in Nigeria. Hence, Beverton-Holt Economic Recruitment model can be
used in modeling macroeconomic time series. Fits and Diagnostics forecast shows value between 2020 to 2023 indicating that Nigeria’s economy is in take off stage, GDP and other Ramonu Abiodun S.JRSS-NIG. Group Vol. 1(2), 2024, pg. 140 - 1642

ISSN NUMBER: 1116-249X
component measure will exhibit’s a sigmoidal growth curve that will begins rapidly before year

STATISTICAL ANALYSIS OF CRIME PATTERNS IN KATSINA METROPOLIS

2024-12-06T16:31:22+01:00

The study investigates the most effective deployment of security personnel in high-crime areas using secondary data from Katsina Police Headquarters, the data comprises twelve types of crimes from various police stations in the Katsina metropolis from 2016 to 2022. The data was analyzed using the Friedman test. The Friedman result was found to be statistically significant, indicating substantial differences in crimes across the types of crime.
Automobile theft and assault are the most occurring crimes with the highest mean ranks, while kidnapping and unnatural offences are the least frequent crimes in the areas. The study reveals that 33 out of every 1000 people commit at least one crime in the Katsina metropolis,
with the most common crimes being automobile theft, assault, and housebreaking. The study suggests that the government should increase the deployment of security personnel in the GRA, Sabon Gari, and Central police stations during festive months like December, January, and June.

DECADAL ANALYSIS OF TWIN BIRTHS IN NIGERIA WITH ITS SPACE-TIME TREND.

2024-12-06T16:36:18+01:00

Nigeria has one of the highest twin birth rates in the world, yet it faces significant challenges with high twin mortality. This study explores the spatial and temporal dynamics of twin births in Nigeria between 2008 and 2018, focusing on survival outcomes and contributing factors. Leveraging data from the Nigeria Demographic and Health Surveys (NDHS), a Bayesian space- time model with Gaussian intrinsic Conditional Autoregressive (iCAR) priors was applied, using
Integrated Nested Laplace Approximation (INLA) to analyze twin birth rates, survival patterns, and regional disparities. Results indicate a modest increase in the national twin birth rate, from 3.3% in 2008 to 3.6% in 2018. South West region consistently recorded the highest twin birth rates, rising from 4.3% in
2008 to 4.8% in 2018, followed by the South-South and South-East regions. The state level in- depth analysis showed that Ekiti, Bauchi, and Enugu exhibited exceptionally high twin birth rates throughout the period under review. Despite this growth, twin mortality remained critically high, with over 70% of twins not surviving across both years. A slight improvement in twin survival was observed, increasing from 25.5% in 2008 to 26.4% in 2018.
Spatial analysis revealed significant regional disparities, with states like Delta and Kogi showing a marked decline in twin birth rates over the decade, while states like Ekiti, Bauchi, and Enugu maintained persistently high rates.
The persistently high mortality rate among twins emphasizes the urgent need for targeted healthcare interventions, particularly in regions with higher twin birth rates. Enhanced healthcare strategies are essential to reduce mortality and improve the well-being of twins in Nigeria. This study also recommends further research into the underlying causes driving twin birth rates and survival outcomes, aiming to better inform healthcare policies and practices.

PANEL REGRESSION MODEL ON THE IMPACT OF SOME SELECTED MACROECONOMICS VARIABLES ON GROSS DOMESTIC PRODUCT IN AFRICAN COUNTRIES

2024-12-06T16:38:44+01:00

The Gross Domestic Product (GDP) of a country is a key indicator of its economic
performance and growth. In Africa, understanding the factors that influence GDP is
crucial for policymakers to make informed decisions that promote economic
development. This study aims to investigate the impact of these selected macroeconomic
variables on GDP in African countries using panel regression analysis. The study
investigates the relationship between selected microeconomic variables and gross
domestic product in African countries. Using panel regression models, the study analyzes
the impact of consumer price index (CPI), interest rate (IR), exchange rate (ER), and
trade balance (TB), on GDP across 54 African countries over the period 2010-2023. The
study specifically employs pooled regression, fixed effects (FE), and random effects (RE)
models. The Hausman specification test result revealed that the fixed- effect model is more
efficient for modelling the impact of some selected microeconomic variables (Housman
specification test statistic = 21.063, p < 0.05). The study revealed further that CPI has positive
and insignificant impact on GDP (B 1 = 0.016, p>0.05) while IR and ER has negative and
insignificant impact on GDP (B 2 = -0.414, p >0.05 and B 4 = -0.005, p >0.05). The study
also revealed that trade balance has positive and significant impact on GDP (B 3 = 3.748,
p<0.05). This implies that increase in trade balance significantly increase gross domestic
product of Africa countries for the period under study. Based on these findings, it was
recommended that government should improve monetary and fiscal policies and also promotes
economic stability.

SOCIOECONOMIC AND REGIONAL DETERMINANTS OF FEMALE GENITAL MUTILATION PREVALENCE IN NIGERIA: A STATISTICAL ANALYSIS USING MULTINOMIAL LOGISTIC REGRESSION

2024-12-23T15:20:36+01:00

Female Genital Mutilation (FGM) is a critical public health concern that perpetuates gender inequality
and poses significant health risks for women and girls. This study aims to investigate the
socioeconomic and regional factors associated with the circumcision status of Nigerian women, thereby
contributing to the understanding of the persistence of FGM in Nigeria. A cross-sectional design was
utilized, employing data from the 2018 Nigeria Demographic and Health Survey (NDHS). A
multinomial logistic regression model was applied to identify potential predictors of circumcision
status, with adjusted odds ratios (AORs) and 95% confidence intervals (CIs) calculated to quantify the
associations. The findings reveal that older age, urban residency, higher educational attainment, and
increased socioeconomic status correlate with a decreased likelihood of circumcision. Additionally,
significant regional variations were observed, with women from the South-East and South-West
regions displaying markedly higher odds of being circumcised compared to their counterparts in other
regions. These results highlight the imperative for targeted public health interventions in Nigeria,
particularly focusing on the regions and demographics most affected by FGM. Strategies should centre
on education and economic empowerment to mitigate the prevalence of this practice and advance
women's rights.