The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed for Osun State monthly rainfall data and the analysis was based on probability time series modeling approach. The Plot of the original data shows that the time series is stationary and the Augmented Dickey-Fuller test did not suggest otherwise. The graph further displays evidence of seasonality and it was removed by seasonal differencing. The plots of the ACF and PACF show spikes at seasonal lags respectively, suggesting SARIMA (1, 0, 1) (2, 1, 1). Though the diagnostic check on the model favoured the fitted model, the Auto Regressive parameter was found to be statistically insignificant and this led to a reduced SARIMA (1, 0, 1) (1, 1, 1) model that best fit the data and was used to make forecast.

This paper explores the relationship between the United Nations (UN) e-government index (EGDI) and e-government development in Africa from 2010 to 2020 and forecasts the effect of e-government on the actualization of the sustainable development goals (SDGs) in Africa by 2030. To achieve the objectives, secondary data were collected on EDGI, online service delivery index (OSI), telecommunication infrastructure index (TII) and human capital index (HCI) from UN e-government survey spanning 2010-2020. The study utilized an improved modelled technique of panel data regression for cross-sectional observations. The finding indicates that there exists a positive and significant impact of the OSI, TII, and HCI on the overall EGDI in Africa. The study also revealed that there is a strong and positive relationship between E-government Development Indicators and the achievement of UN SDGs in Africa. This implies that African nations will experience a slow and insignificant increase from 2022 with an EGDI value of 0.4208 to 0.4331 in 2024, implying a 2.9% slight increment. The predicted value further shows that there will be a decrease from the EGDI value of 0.4331 in 2024 to 0.4330 in 2026, while the average EGDI value will increase slightly to 0.4346 in 2028 and finally to 0.4369 in the year 2030, which is equivalent to a 0.5% increment. EGDI value obtained predicts that Africa may not attain UN SDGs by 2030. Accordingly, the study recommends that drastic measures be taken to improve the three indices.

Corporate effective tax rates (ETR) of large Nigerian listed firms during the new tax regime as well as the influence of firm size, leverage, return on asset, capital intensity, and inventory intensity on corporate effective tax rate was investigated. The study aims to examine the influence of some corporate tax attributes in some selected large public listed firms within and across Nigeria with avoidance of corporate effective tax rates (ETR). The data used in the study was extracted from the 2019 Nigeria Annual Financial reports. The data were collected from the period 2012 to 2018, the period where Nigeria imposed a new tax regime on the current year assessment system (effective from the year 2012). Pooled ordinary least square (POLS), fixed effect and random effect regression were applied to the data, findings from the study revealed that the random effect regression model was preferred for result interpretation. It was also discovered that; firm size (FSIZE), return on assets (ROA), and inventory intensity (INVINT) have a positive and significant influence on corporate effective tax rates (ETR) avoidance. Firm leverage (LEV) and capital intensity (CAPINT) on the other hand has an insignificant inverse influence on effective tax rates (ETR). Hence, this study suggested that firm size (FSIZE), return on assets (ROA), and inventory intensity (INVINT) is the major variable that positively influences the corporate effective tax rates (ETR) the listed large firms in Nigeria.

The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

We investigated the finite properties as well as the goodness of fit test for the cubic smoothing spline selection methods like the Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV) and Mallow CP criterion (MCP) estimators for time-series observation when there is the presence of Autocorrelation in the error term of the model. The Monte-Carlo study considered 1,000 replication with six sample sizes: 30; 60; 120; 240; 480 and 960, four degree of autocorrelations; 0.1; 0.3; 0.5; and 0.9 and three smoothing parameters; lambdaGML= 0.07271685, lambdaGCV= 0.005146929, lambdaMCP= 0.7095105. The cubic smoothing spline selection methods were also applied to a real-life dataset. The Predictive mean square error, R-square, and adjusted R-square criteria for assessing finite properties and goodness of fit among competing models discovered that the performance of the estimators is affected by changes in the sample sizes and autocorrelation levels of the simulated and real-life data set. The study concluded that the Generalized Cross-Validation estimator provides a better fit for Autocorrelated time series observation. It is recommended that the GCV works well at the four autocorrelation levels and provides the best fit for time-series observations at all sample sizes considered. This study can be applied to; non –parametric regression, non –parametric forecasting, spatial, survival and econometric observations.