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Item On the modification of M-out-of-N bootstrap method for heavy-tailed distributions(Global Society of Scientific Research and Researchers, 2015) Opayinka, H. F.; Adepoju, A.A.This paper is on the modification of 𝑚-out-of-𝑛 bootstrap method for heavy-tailed distributions such as income distribution. The objective of this paper is to present a modified 𝑚-out-of-𝑛 bootstrap method (𝑚𝑚𝑜𝑛) and compare its performance with the existing m-out-of-n bootstrap method (𝑚𝑜o𝑛). The nature of the upper tail of a distribution is the major reason for the poor performance of classical bootstrap methods even in large samples. The ‘𝑚𝑚𝑜𝑛’ bootstrap method was therefore, proposed as an alternative method to ‘𝑚𝑜𝑛’ bootstrap method. The distribution involved has finite variance. The simulated data sets used was drawn from Singh-Maddala distribution. The methodology involved decomposing the empirical distribution and sampling only n⃛ times with replacement from a sample size n, such that n⃛ →∞ as n→∞, and n⃛/n →0. The performances are judged using standard error; absolute bias; coefficient of variation and root mean square error. The findings showed that ‘𝑚𝑚𝑜𝑛’ performed better than 𝑚𝑜𝑛 in moderate and larger samples and it converged fasterItem A time varying parameter state-space model for analyzing money supply-economic growth nexus(2015-03) Awe, O.O.; Crandell, I.; Adepoju, A.A.; Leman, S.In this paper, we propose a time-varying parameter state space model for analyzing predictive nexus of key economic indicators such as money supply and Gross Domestic Product (GDP). Economic indicators are mainly used for measuring economic trends. Policy makers in both advanced and developing nations make use of economic indicators like GDP to predict the direction of aggregate economic activities. We apply the Kalman filter and Markov chain Monte Carlo algorithm to perform posterior Bayesian inference on state parameters specified from a discount Dynamic Linear Model (DLM), which implicitly describes the relationship between response of GDP and other economic indicators of an economy. In our initial exploratory analysis, we investigate the predictive ability of money supply with respect to economic growth, using the economy of Nigeria as a case study with an additional evidence from South African economy. Further investigations reveal that leading variables like capital expenditure, the exchange rate, and the treasury bill rate are also useful for forecasting the GDP of an economy. We demonstrate that by using these various regressors, there is a substantial improvement in economic forecasting when compared to univariate random walk modelsItem Robustness of simultaneous estimation methods to varying degrees of correlation between pairs of random deviates(International Research Publication House, 2010) Oyamakin, O.; Adepoju, A.A.This study examined how six estimation methods of a simultaneous equation model cope with varying degrees of correlation between pairs of random deviates using the Variance and Total Absolute Bias (TAB). A two-equation simultaneous system was considered with assumed covariance matrix. The model was structured to have a mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios of r = 0.0, 0.3 and 0.5. The performances of various estimators considered were examined at various sample sizes, correlation levels and 50 replications. The sample size, N = 20,25,30 each replicated 50 times was considered. OLS is performed best when the variance is used to study the finite sample properties of the estimators in that it produces the least variances in all the cases considered and at all sample sizes. All the estimators revealed an asymptotic pattern under CASE I.Item Ordinal logistic regression model: an application to pregnancy outcomes(Science Publications, 2010) Adeleke, K.A.; Adepoju, A.A.Problem statement: This research aimed at modeling a categorical response i.e., pregnancy outcome in terms of some predictors, determines the goodness of fit as well as validity of the assumptions and selecting an appropriate and more parsimonious model thereby proffered useful suggestions and recommendations. Approach: An ordinal logistic regression model was used as a tool to model the three major factors viz., environmental (previous cesareans, service availability), behavioral (antenatal care, diseases) and demographic (maternal age, marital status and weight) that affected the outcomes of pregnancies (livebirth, stillbirth and abortion). Results: The fit, of the model was illustrated with data obtained from records of 100 patients at Ijebu-Ode, State Hospital in Nigeria. The tested model showed good fit and performed differently depending on categorization of outcome, adequacy in relation to assumptions, goodness of fit and parsimony. We however see that weight and diseases increase the likelihood of favoring a higher category i.e., (livebirth), while medical service availability, marital status age, antenatal and previous cesareans reduce the likelihood/chance of having stillbirth. Conclusion/Recommendations: The odds of being in either of these categories i.e., livebirth or stillbirth showed that women with baby’s weight less than 2.5 kg are 18.4 times more likely to have had a livebirth than are women with history of babies 2.5 kg. Age (older age and middle aged) women are one halve (1.5) more likely to occur than lower aged women, likewise is antenatal, (high parity and low parity) are more likely to occur 1.5 times than nullipara.Item Ranking of simultaneous equation techniques to small sample properties and correlated random deviates(Science Publications, 2009) Adepoju, A.A.; Olaomi, J.O.Problem statement: All simultaneous equation estimation methods have some desirable asymptotic properties and these properties become effective in large samples. This study is relevant since samples available to researchers are mostly small in practice and are often plagued with the problem of mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The objective of this research was to study the small sample properties of these estimators when the errors are correlated to determine if the properties will still hold when available samples are relatively small and the errors were correlated. Approach: Most of the evidence on the small sample properties of the simultaneous equation estimators was studied from sampling (or Monte Carlo) experiments. It is important to rank estimators on the merit they have when applied to small samples. This study examined the performances of five simultaneous estimation techniques using some of the basic characteristics of the sampling distributions rather than their full description. The characteristics considered here are the mean, the total absolute bias and the root mean square error. Results: The result revealed that the ranking of the five estimators in respect of the Average Total Absolute Bias (ATAB) is invariant to the choice of the upper (P1) or lower (P2) triangular matrix. The result of the FIML using RMSE of estimates was outstandingly best in the open-ended intervals and outstandingly poor in the closed interval (- 0.05Item A comparison of least squares dummy variable (LSDV) and the pooled estimator in fixed effect model(2008) Olayiwola, O. M.; Adepoju, A.A.; Olajide, J. TThis paper examines a comparison of Least Squares Dummy Variable and a pooled estimator in a fixed effect model. The aims of the research are: to estimate the individuals firms parameters by using least squares dummy variables. To estimate parameter of the pooled observations using ordinary least squares. To estimate the behavioral relationship between individuals variables and to test for the significance différence across the groups. The framework was based on a fixed effect model. The analysis of a panel model was carried out using the Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) methods. various tests were carried out to determine which of the methods to use when dealing with a panel data. The results of the analysis showed significant différence across the different groups effect. F is also significant at 95% level by using either the fixed effect or pooled model in a panel data. Also, a measure of fit of the model carried out showed that the fixed effect model significantly explained the variation in the dependent variable while pooling the model explained a very small proportion of the total variation in the dépendent variable. In the light of the above, it will be appropriate to use a fixed effect least squares dummy variable rather than pooling the data in the analysis of a panel data