Objective: This study is focused on the analysis of the spread of Covid-19 in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection. Methodology: Daily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria. Results: The ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box–Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame. Conclusion: Federal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of Covid-19 in Nigeria.

In the last decade, the size and complexity of datasets have expanded significantly, necessitating more sophisticated predictive methods. Despite this growth, limited research has been conducted on the effects of autocorrelation within widely used regression methods. This study addresses this gap by investigating how autocorrelation impacts the predictive accuracy and efficiency of six regression approaches: Artificial Neural Network (ANN), Ordinary Least Squares (OLS), Cochrane–Orcutt (CO), Prais–Winsten (PW), Maximum Likelihood Estimation (MLE), and Restricted Maximum Likelihood Estimation (RMLE). The study evaluates each method’s performance on three datasets characterized by autocorrelation, comparing their predictive accuracy and variability. The analysis is structured into three phases: the first phase examines predictive accuracy across methods using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE); the second phase evaluates the efficiency of parameter estimation based on standard errors across methods; and the final phase visually assesses the closeness of predicted values to actual values through scatter plots. The results indicate that the ANN consistently provides the most accurate predictions, particularly in large sample sizes with extensive training data. For GDP data, the ANN achieved an MSE of 1.05 × 10⁹, an MAE of 23,344.64, and an MAPE of 81.66%, demonstrating up to a 90% reduction in the MSE compared to OLS. These findings underscore the advantages of the ANN for predictive tasks involving autocorrelated data, highlighting its robustness and suitability for complex, large-scale datasets. This study provides practical guidance for selecting optimal prediction techniques in the presence of autocorrelation, recommending the ANN as the preferred method due to its superior performance.

Efficiency estimation in production technology has been a concern in economics, with methodologies such as Stochastic Frontier Analysis (SFA) playing a key role in this area. SFA has been pivotal in evaluating the efficiency of entities by isolating technical inefficiency from random production errors. However, despite its significance, the application of SFA faces challenges when the multicollinearity assumption underlying the model is violated. Therefore, this study presents a novel estimator, termed “Principal Component Analysis Estimation for Stochastic Frontier Analysis” (PCA-SFA), to address the problem of multicollinearity in the classical SFA model. The PCA-SFA estimator integrates Principal Component Analysis (PCA) to correct assumption violation arising from multicollinearity. Monte Carlo simulation study was conducted to ascertain the PCA-SFA performance, involving no fewer than 2,000 replications based on the Cobb-Douglas production function with varying levels of multicollinearity, represented by correlation coefficients ranging from 0.8, 0.9, 0.95, 0.99, and 0.999, and sample sizes (n) of 20, 50, 100, 250, and 1,000. The proposed estimator’s performance was compared to the classical SFA model using the Mean Square Error (MSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) as evaluation metrics. The results demonstrate that the PCA-SFA estimator consistently outperforms the classical SFA model. The PCA-SFA model showed significantly lower MSE, AIC, and BIC values, indicating improved precision and reliability in parameter estimation. The study, therefore, recommends that researchers and practitioners in econometrics and related fields consider integrating PCA-SFA into their production efficiency analytical frameworks, particularly when dealing with datasets prone to multicollinearity issues.

Assumptions in the classical linear regression model include that of lack of autocorrelation of the error terms and the zero covariance between the explanatory variable and the error terms. This study is channeled towards the estimation of the parameters of the linear models for both time series and cross-sectional data when the above two assumptions are violated. The study used the Monte-Carlo simulation method to investigate the performance of six estimators: ordinary least square (OLS), Prais-Winsten (PW), Cochrane-Orcutt (CC), Maximum Likelihood (MLE), Restricted Maximum- Likelihood (RMLE) and the Weighted Least Square (WLS) in estimating the parameters of a single linear model in which the explanatory variable is also correlated with the autoregressive error terms. Using the models’ finite properties(mean square error) to measure the estimators’ performance, the results shows that OLS should be preferred when autocorrelation level is relatively mild (ρ = 0.3) and the PW, CC, RMLE, and MLE estimator will perform better with the presence of any level of AR (1) disturbance between 0.4 to 0.8 level, while WLS shows better performance at 0.9 level of autocorrelation and above. The study thus recommended the application of the various estimators considered to real-life data to affirm the results of this simulation study.

Efficiency analysis in production units has long been a key area of interest in economics, particularly with the development of methodologies like Stochastic Frontier Analysis (SFA). Originating from seminal works by Aigner & Cain (1977) and Meeusen & Van Den Broeck (1977), SFA has been instrumental in assessing the efficiency of entities by separating technical inefficiency from random production fluctuations. Despite its widespread application, the SFA model faces challenges, especially when underlying assumptions such as multicollinearity and heteroscedasticity are violated. This study introduces a novel estimator called "Weighted Principal Component Analysis Estimation for Stochastic Frontier Analysis" (WPCA-SFA), which combines the methodologies of weighted least square estimation (WLS) and principal component analysis (PCA) to jointly address these assumption violations. Monte Carlo simulation experiments were conducted to evaluate the performance of the proposed estimator. The results demonstrate that the WPCA-SFA estimator significantly outperforms the standard SFA model by effectively mitigating the adverse effects of both heteroscedasticity and multicollinearity. Based on the findings, the study recommend that researchers and practitioners in the field of efficiency analysis consider employing the WPCA-SFA estimator, particularly in scenarios where multicollinearity and heteroscedasticity are likely to compromise the accuracy of parameter estimations. Neglecting these issues can lead to suboptimal results, whereas the WPCA-SFA has proven to provide more reliable and accurate predictions. This advanced correction methodology should be adopted to enhance the robustness of empirical analyses in stochastic frontier models.

Background : Agriculture remains central to low- and middle-income countries’ (LMICs’) economies but is constrained by low productivity and limited value addition. Special Agro-Industrial Processing Zones (SAPZs) and related agro-industrial parks have emerged as mechanisms for structural transformation, yet evidence on their governance, implementation, and outcomes remains fragmented. This scoping review, therefore, aimed to map the evidence on SAPZs in LMICs, with a particular focus on their governance and institutional models, implementation status, and reported outcomes. Methods : A scoping review was conducted using the Arksey and O’Malley framework as enhanced by Levac et al ., reported following PRISMA-ScR, with Population–Concept–Context eligibility criteria. PubMed, Google Scholar, AgEcon Search, CAB Abstracts, Scopus, and 10 organisational repositories were searched for English-language peer-reviewed and grey literature published from 2000 to 2026. Records were screened in Rayyan by three reviewers, charted, and appraised with design-specific tools (AACODS, SANRA, MMAT, and JBI). Findings were synthesised descriptively and thematically. Results : Out of the 2191 records identified, 27 were included. SAPZs’ maturity was heterogeneous, spanning planning, implementation, and operational stages concurrently. Public-led governance dominated at planning, diversifying towards specialised authorities, private investors, and producer organisations as SAPZs matured, with financing shifting correspondingly from public and development partner sources towards blended and commercial investment. Outcomes were predominantly projected rather than measured. Market access and exports had the strongest empirical support, whereas food security and social inclusion showed marked ambition. Weak institutional coordination and unreliable raw material supply were the most consistently reported barriers. Conclusion : Our findings support viewing agro-industrial processing zones as an evolving policy instrument whose development warrants stronger independent and longitudinal evaluation before their effectiveness and longer-term development benefits can be established. Progress towards operation does not, by itself, establish success, and current evidence remains insufficient to determine whether anticipated benefits are sustained or equitably distributed. Future research should prioritise independent, zone-level, and longitudinal evaluation, with standardised outcome measures and clearer attribution, to establish whether substantial investments translate into sustained agro-industrial transformation.