Estimation of Parameters of Linear Econometric Model and the Power of Test in the Presence of Heteroscedasticity Using Monte-Carlo Approach

This paper is concerned with the estimation of parameters of linear econometric model and the power of test in the presence of heteroscedasticity using Monte-Carlo approach. The Monte Carlo approach was used for the study in which random samples of sizes 20, 50 and 100, each replicated 50 times were generated. Since the linear econometric model was considered, a fixed X variable for the different sample sizes was generated to follow a uniform distribution while 50 replicates of the stochastic error term for different sample sizes followed a normal distribution. Two functional form of heteroscedasticity  were introduced into the econometric model with the aim of studying the behaviour of the parameters to be estimated. 50 replicates of the dependent variable for each sample size was generated from the model  where the parameters,  were assumes to be 0.5 and 2.0 respectively. The Ordinary Least Squares (OLS) and the Generalized Least Squares (GLS) estimators were studied to identify which is more efficient in the presence of the two functional forms of heteroscedasticity considered. Both estimators were unbiased and consistent but none was convincingly more efficient than the other. The power of test was used to examine which test of heteroscedasticity (i.e., Glejser, Breusch-Pagan and White) is most efficient in the detection of any of the two forms of heteroscedasticity using different sample sizes. Glejser test detects heteroscedasticity more efficiently even in small sample sizes while White test is not as efficient when sample size is small compared to when the sample size is large.   Keywords: Heteroscedasticity, Monte Carlo, Power of Test, Ordinary Least Squares Estimator, Generalized Least Squares Estimator, Breusch-Pagan test, Glejser test, White test, Bias, Variance, Root Mean Square Erro

DOES MACRO ECONOMIC VARIABLES HAVE EFFECT ON STOCK MARKET MOVEMENT IN NIGERIA?

Over the past decades, numerous studies have analyzed the relationship and the different results obtained from these studies have motivated further research. The relationship between Average share price and macro – economic variable has been well documented for the developed markets. However, this paper seeks to address the question of whether macro – economic variables have a significant with stock market movement using time series annual data for the period from 1985 – 2008. The selected macro – economic variables for study include external debt, inflation rate, real interest rate, investment, and exchange rate. The research entails the use of Argumented Dickey Fuller test, multivariate cointegration test, vector error correction, variance decomposition and causality analysis. The result was that all the variables were stationary at 2nd difference, four cointegrating equations were present i.e. long run relationship exists between the selected macro –economic variable and average share price. All macro – economic variables were insignificant but all negative relationship with ASP but only External debt was significant related to ASP. ASP and External debt were found to granger cause in pairs while an independent causality exists between the selected macro – economic variable and ASP. These show that ASP is not a leading indicator for the selected macro – economic variable. Keywords: Macro-economic, Stock market, Inflation rate, multivariate cointegration test, External deb

FITTING THE STATISTICAL DISTRIBUTION FOR DAILY RAINFALL IN IBADAN, BASED ON CHI-SQUARE AND KOLMOGOROV- SMIRNOV GOODNESS-OF-FIT TESTS.

This paper presents several types of statistical distributions to describe rainfall distribution in Ibadan metropolis over a period of 30 years.The exponential, gamma, normal and poisson distributions are compared to identify the optimal model for daily rainfall amount based on data recorded at rain guage station at Forestry Research Institute of Nigeria, Jericho, Ibadan (FRIN). The models are evaluated based on chi- square and kolmogorov-smirnov tests. Overall, this study has shown that the exponential distribution is the best model followed by normal and poisson model that has the same estimated rainfall amount for describing the daily rainfall in Ibadan metropolis. Keywords: scale parameter, asymptotically, exponential distribution, gamma distribution, poisson and kolmogorov-smirnov

Effect of Multicolinearity and Autocorrelation on Predictive Ability of Some Estimators of Linear Regression Model

Violation of the assumptions of independent regressors and error terms in linear regression model has respectively resulted into the problems of multicollinearity and autocorrelation. Each of these problems separately has significant effect on parameters estimation of the model parameters and hence prediction.  This paper therefore attempts to investigate the joint effect of the existence of multicollinerity and autocorrlation on Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis on prediction of linear regression model through Monte Carlo studies using the adjusted coefficient of determination goodness of fit statistic of each estimator. With correlated normal variables as regressors, it further identifies the best estimator for prediction at various levels of sample sizes (n), multicollinearity  and autocorrlation . Results reveal the pattern of performances of COR and ML at each level of multicollinearity over the levels of autocorrelation to be generally and evidently convex especially when  and while that of OLS and PC is generally concave. Moreover, the COR and ML estimators perform equivalently and better; and their performances become much better as multicollinearity increases. The COR estimator is generally the best estimator for prediction except at high level of multicollinearity and low levels of autocorrelation. At these instances, the PC estimator is either best or competes with the COR estimator. Moreover, when the sample size is small (n=10) and multicollinearity level is not high, the OLS estimator is best at low level of autocorrelation whereas the ML is best at moderate levels of autocorrelation. .Keywords: Prediction, Estimators, Linear Regression Model, Multicollinearity, Autocorrelation

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Co(II) Complex of Mefloquine Hydrochloride: Synthesis, Antimicrobial Potential, Antimalaria and Toxicological Activities

Transition metal complex of Co(II) with Mefloquine hydrochloride (antimalaria drug) was synthesized using template method. Chemical analysis including conductivity measurements and spectroscopic studies were used to propose the geometry and mode of binding of the ligand to metal ion. From analytical data, the stoichiometry of the complex has been found to be 1:1. Infrared spectral data also suggest that the ligand (mefloquine hydrochloride) behaves as a tridentate ligand with N:N:O donor sequence towards the metal ion. The complex generally showed octahedral coordinate geometry. Conductivity measurement of 10-2 mol dm-3 methanol solution of the complex indicated non-electrolytic nature of metal complex. It also revealed that the ligand anions were covalently bonded to the complex. In-vivo evaluation of antimicrobial studies of the metal complex showed greater activities when compared to the free mefloquine.The complex was screened against malarial parasites (Plasmodium yoelii nigeriensis): It was evident from the results obtained that Co(II) mefloquine has highest clearance of about 80% parasitaemia reduction compared to the free mefloquine. The ligand and metal complex were screened for their toxicological activities at the dose of 0.60 mg/Kg body weight twice daily for seven days on the alkaline phosphatase (ALP), alanine aminotranferase (ALT), and aspartate aminotransferase (AST) activities of rat serum, liver and kidney. Overall, it was revealed that both mefloquine and its metal complex do not showed toxicity particularly on the liver and kidney