| 1 |
Author(s):
Okolie Ifeyinwa Juliana, Olanrewaju Samuel Olayemi, Oguntade Emmanuel Segun.
Page No : 1-17
|
Robust Estimation Techniques in Panel Data Models in the Presence of Multicollinearity, Heteroscedasticity, and Autocorrelation Violations.
Abstract
This study proposed and evaluated three novel robust estimators—Robust Shrinkage Generalized Method of Moments (RSGMM), Panel Adaptive Ridge GMM (PARGMM), and Heteroscedasticity-Autocorrelation-Robust Shrinkage GMM (HARSGMM)—for panel data models where classical assumptions are frequently violated. The estimators were designed to simultaneously address multicollinearity, heteroscedasticity, and autocorrelation, which commonly undermine the reliability of conventional estimators such as Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS), First Difference (FD), and Between Estimators (BTW). Using Monte Carlo simulations, the performance of all estimators were assessed across three scenarios of increasing violation severity and varying sample sizes. Performance metrics include bias, variance, mean squared error (MSE), and efficiency. Results revealed that HARSGMM and RSGMM consistently outperformed traditional estimators in terms of lower bias and MSE, particularly in settings with high assumption violations and larger samples. Even under baseline conditions with minimal violations, the proposed estimators maintained superior efficiency. These findings support the adoption of HARSGMM and RSGMM as more reliable alternatives for empirical researchers dealing with complex panel datasets. The study concluded with recommendations for broader application and integration of these robust techniques into econometric software and policy-oriented research.
| 2 |
Author(s):
Victor Mimoh Mazona, Femi Barnabas Adebola, Vincent Odiaka.
Page No : 18-32
|
Gumbel-Exponential Distribution: Its Properties and Application.
Abstract
This paper presents a mixture distribution of a new modelling tool,which is termed Gumbel-Exponential (GEXP) distribution. The distribution allows us to capture some real characteristics of data and it is an important tool for understanding the phenomenon. The various statistical properties of this distribution were fully explored and discussed, these include; the mean, variance, moments, mode, reliability function and hazard function.The worth of the mixing distribution has been demonstrated by applying it to real life data.
| 3 |
Author(s):
Ekakitie Omamoke, Omamoke Layefa.
Page No : 33-43
|
A Comparative Analysis of the Malthusian Growth Model and Linear Regression Approach in Predicting Malaria Infection.
Abstract
The study shows a comparative analysis between Malthusian growth model and simple linear regression approach in predicting the outcome of malaria infection. These two models was applied in predicting the number of persons infected with malaria in Nigeria in the nearest future (2023 to 2030) using data from 2019 to 2022. The outcome revealed that at the end of 2030 the population of persons that will be infected with malaria using the regression model was One hundred and five million (105, 000, 000) while that of Malthusian model was Ninety one million, five hundred and thirty (91, 530, 000). Furthermore, results gotten from Malthusian model proved to be more reliable than the results gotten from the regression model because the coefficient of correlation and Residual sum of squares for Malthusian model was 0.999 and 1801670629370.527 with the data showing a level of statistical significance compared to the simple linear regression model approach which was 1.00 and 0.00 respectively. This research will assist government organizations; non-government organizations, private and health organization to apply a proactive measure and plan in dealing with the issue of malaria infection in the nation
| 4 |
Author(s):
Bolarinwa B. T., Yahaya H. U., Adehi M. U..
Page No : 44-53
|
Robust Arima-Gas Modeling of Intradaily Financial Data with Structural Breaks and Jumps.
Abstract
This article investigates the robustness of ARIMA-GAS model to structural break and jumps, through simulation. It examines abrupt regime change by introducing a deterministic structural break at t=500 in both the ARIMA and GAS dynamics. The sample size is n=1000 with an 80/20 estimation–evaluation split; one-step-ahead forecasts are generated in a rolling fashion. For the jump process, this scenario injects discontinuous jumps into an otherwise Gaussian environment to test robustness to rare but large shocks. The data generating process is ARIMA (1,1,1) with score dynamics(A_1,B_1)=(0.35,0.55). Innovations are with jump intensity λ=0.05. We generate n=1000 observations with differencing order d=1 and evaluate one-step-ahead forecasts on the final 20% of the sample using rolling updates. The study utilizes ARIMA, GAS, LSTM and GARCH as benchmarks. For the pre-break regime, ARIMA outperformed the rest models on the basis of both the root mean square error (rmse) and mean absolute error (mae), closely followed by ARIMA-GAS. Pure GAS performs better than GARCH and LSTM which outperformed GARCH. Contrary to the pre-break case in which the classical ARIMA takes the lead, ARIMA-GAS takes the lead, achieves the lowest average loss (least rmse) in the post-break era beating ARIMA to the last position. LSTM is competitive, establishing its relevance in the competition, occupying the second position. GAS model maintains its third position, beating GARCH. Results of multi-horizon forecasting (h=1, 5, 10) reveal on the basis of rmse, ARIMA-GAS as best, followed by LSTM, although LSTM narrows the gap at longer horizons. An examination of the effect on model accuracy, of proportion of series length used for training reflects that all models experience improved accuracy with increased training data length; LSTM gains relatively more, yet ARIMA–GAS retains the lowest average RMSE. With jumps, ARIMA-GAS performed better than benchmarks having the least mse of 1.48562 and mae of 1.10234. The GAS model is next, confirming the capacity of GAS model to capture jumps. Classical ARIMA is next to GAS. Their combination has outperformed them individually and other benchmarks. This further confirms the appropriateness of combining GAS with ARIMA. ARIMA-GAS model outperforms benchmarks in multi-horizon forecasting comparison on the basis of rmse and mae— a feat repeated when two other jump intensity values (λ=0.01,0.1) are used to asses sensitivity, although relative performances of the benchmarks are altered. Based on the performance of ARIMA-GAS model over the benchmarks in the presence of structural breaks and jumps, the model offers a promising approach to modeling intradaily financial data with such features.
| 5 |
Author(s):
Consul Juliana Iworikumo, Okenwe Idochi.
Page No : 54-63
|
Determination of Covariate Effects and Risk Ratio of Divorce in Nigeria.
Abstract
In this research, the factors contributing to divorce are examined using the Cox proportional fit. The Schoenfeld residual was used to test for the assumption of proportionality. This research determined the relationship between the observed covariates and divorce and the risk ratio of divorce among the couples. A data set of 111 samples from the Rivers State Judiciary High court who filed for divorce was used for this research. Some relevant observed covariates were the ages, profession and educational level of the couples, the presence of children, the number of counselling sessions, the number of court sittings, the time of the marriage and time of divorce. Results from this research revealed that the ages of the couples led to a slight increase in the risk of divorce and not having children had a greater risk of divorce. The risk of divorce decreased as the employment status of the couples changed since the employment status is related to the financial status of the couples. The risk of divorce increased as the educational level of the couples changed from illiteracy to a higher level of education. The result also revealed that divorce was not independent of the number of counselling sessions or number of sittings. The number of counselling and number of sittings are highly significant. This research highly recommends that the Government should train more professional marriage counsellors and establish marriage counselling centres in most cities where seminars, conferences, workshops and enlightenment programmes are organised with the aim of reducing the divorce among couples.
| 6 |
Author(s):
Eduma Enobong Essien, Joseph Dagogo, Biu Emmanuel Oyinebifun.
Page No : 64-97
|
Vector Autoregressive (VAR) Model Forecasting of the Petroleum Products Price Rate in Some Nigeria State.
Abstract
This study, titled “Vector Autoregressive (VAR) Model Forecasting of Petroleum Products Price Rate in Some Nigerian States (Petrol, Diesel, and Kerosene)”, examines the trend, interdependence, and future prediction of petroleum product prices across two Nigerian states. The research evaluates the stationarity and descriptive statistics of the products’ price distributions. Petroleum remains Nigeria’s major revenue source, contributing over 90% annually. However, the 2012 national debate on petroleum pricing exposed inconsistencies in cost determination across agencies, enabling corruption within the subsidy framework. Analysis revealed that prices of petrol, diesel, and kerosene have continued to rise due to inefficiencies and distortions in the distribution chain. The yearly and monthly mean plots (2017–2024) indicate upward trends. Yearly mean plots for Petrol, Diesel, and Kerosene show quadratic trends with coefficients of determination (R²) of 97.2%, 96.1%, and 98.8%, respectively. Monthly mean plots for Petrol and Diesel also exhibit quadratic trends (R² = 90.8% and 92.8%), while Kerosene’s monthly mean shows a linear trend (R² = 86.8%). Correlation analyses and cross-correlation functions show strong positive relationships among the three petroleum products. The series for all products were non-stationary and became stationary after first differencing. In the estimated VAR(2) models, when Petrol Prices served as the dependent variable, only Kerosene Prices were significant at the 5% level; a 1% change in Kerosene Prices caused about a 7.7% change in Petrol Prices. Similarly, when Diesel Prices were the dependent variable, Kerosene Prices remained the only significant predictor, where a 1% change in Kerosene Prices caused a 39.3% change in Diesel Prices. Conversely, when Kerosene Prices were the dependent variable, both Petrol and Diesel Prices were significant, with 1% changes in Petrol and Diesel Prices causing 85.0% and 18.3% changes in Kerosene Prices, respectively. Forecast results indicate that Petrol Prices will experience a slight upward and stable variation, Diesel Prices will trend upward, and Kerosene Prices will fluctuate gradually from 2025 to 2027. Predicted prices range from ₦183.19–₦225.93 for Petrol, ₦191.35–₦1121.25 for Diesel, and ₦451.76–₦1866.88 for Kerosene.
| 7 |
Author(s):
Boniface Inalu Obi, Yomi Monday Aiyesimi.
Page No : 98-108
|
Thin Film Flow of Non-Newtonian Fluid with Temperature-Dependent Viscosity in Pipe.
Abstract
The analysis of thin film flow of non-Newtonian fluid with Vogel model viscosity in cylindrical pipe is considered. Vogel model is introduced to account for the temperature-dependent viscosity. Perturbation technique is employed to solve the dimensionless nonlinear momentum and energy equations. Results indicate that increase in the magnetic field parameter increases both the flow velocity and the temperature of the cylinder and a little increase in the Brinkman number greatly increased the temperature of the plate. Results further show that increase in the parameter M, slightly reduced the flow velocity and increase in the parameter B, drastically reduced the velocity of the fluid flow. It is observed that as B increases, the cylindrical wall temperature is greatly lowered which means that particles loose more thermal energy that would hitherto help in overcoming the attractive forces holding them together.
| 8 |
Author(s):
Ekemini U. George, Akaninyene U. Udom, Matthew J. Iseh, Anthony E. Usoro.
Page No : 109-125
|
Convergence Result for Random Split Variational Inequality Problem Using a Two-Step Iterative Scheme in Hilbert Spaces.
Abstract
The paper focuses on finding solution to stochastic split variational inequality problem, by extending the works done in classical functional analysis, to accommodate situations where there are perturbations in the system, as real life problems are, mostly, random in nature. Solution to the problem is sought through fixed point theory. A random two-step version of the hybrid steepest descent iterative scheme is used to obtain a fixed point solution to the problem. A numerical example is presented, to illustrate the workability of the result. A strong convergence result is also proven for the solution. This result extends, and generalizes some established results in literature on classical functional analysis.
