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Item Regression methods in the presence of heteroscedasticity and outliers(Academia Publishing, 2017-12) Adepoju, A. A .; Ogundunmade, T.P .; Adebayo, K. B.It has been observed over the years that real life data are usually non-conforming to the classical linear regression assumptions. One of the stringent assumptions that is unlikely to hold in many applied settings is that of homoscedasticity. When homogenous variance in a normal regression model is not appropriate, invalid standard inference procedure may result from the improper estimation of standard error when the disturbance process in a regression model present heteroscedasticity. When both outliers and heteroscedasticity exist, the inflation of the scale estimate can deteriorate. This study identifies outliers under heteroscedastic errors and seeks to study the performance of four methods; ordinary least squares (OLS), weighted least squares (WLS), robust weighted least squares (RWLS) and logarithmic transformation (Log Transform) methods to estimate the parameters of the regression model in the presence of heteroscedasticity and outliers. Real life data obtained from the Central Bank of Nigeria Bulletin and Monte Carlo simulation were carried out to investigate the performances of these four estimators. The results obtained show that the transformed logarithmic model proved to be the best estimator with minimum standard error followed by the robust weighted least squares. The performance of OLS is the least in this orderItem Effects of atypical observations on the estimation of seemingly unrelated regression model(Science and Education Publishing, 2017) Adepoju, A. A.; Akinwumi, A. OThe Seemingly Unrelated Regression Equation model is a generalization of a linear regression model that consists of several regression equations in order to achieve efficient estimates. Unfortunately, the assumptions underlying most SUR estimators give little/no consideration to outlying observations which may be present in the data. These atypical observations may have some apparent distorting effects on the estimates produced by these estimators. This study thus examined the effect of outliers on the performances of SUR and OLS estimators using Monte Carlo simulation method. The Cholesky method was used to partition the variance-covariance matrix by decomposing it into the upper and lower non-singular triangular matrices. Varying degree of outliers; 0%, 5%, and 10% were each introduced into five sample sizes; 20, 40, 60, 100 and 500 respectively. The performances of the estimators were evaluated using Absolute Bias (ABIAS) and Mean Square Error (MSE). The results showed that at 0% outliers (when outliers were absent), the ABIAS and MSE of the SUR and OLS estimators showed similar results. At 5% and 10% outliers, the magnitude in ABIAS and MSE for both estimators increased but the SUR estimator showed better performance than the OLS estimator. As the sample size increases, ABIAS and MSE of the estimators decreased consistently for the various degrees of outliers considered with SUR consistently better than OLSItem Ranking of simultaneous equation estimators to outliers from heavy-tailed quasi-uniform distribution(2012-11) Oseni, B.M.; Adepoju, A. A.; Olubusoye, O. E.In this work, the ranking of the performances of two-equation simultaneous models when outliers are presumed present in a convoluted exogenous variable is carried out. The exogenous variable is a convolution of normal and uniform distribution. Monte Carlo experiment was carried out to investigate the performances of four estimators namely: Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), Limited Information Maximum Likelihood (LIML) and Three Stage Least Squares (3SLS). Five sample sizes were used to allow for measure of asymptotic properties of these estimators. The experiment was replicated 1000 limes and the results were evaluated using Total Absolute Bias (TAB), Variance and Root Mean Squared Error (RMSE). It is observed that the performances of the estimators when lower triangular matrix is used are better than that of upper triangular matrix. OLS using TAB as evaluation criterion is better than the other estimators when an exogenous variable is convoluted for the just-identified equation. The performance of 2SLS is best for the over-identified equation. OLS possesses the least variance for both equations and both matrices while LIML has the worst variance in most crises. OLS possesses the smallest RMSE for both matrices and equations except with the over-identified equation using lower triangular matrix when an exogenous variable is convolutedItem Assessment of simultaneous equation techniques under the influence of outliers(2011) Oseni, B. M.; Adepoju, A. A.Most simultaneous equations estimation techniques are based on the assumptions of normality which gives little consideration to some atypical data often called outliers which may be present in the observations. The outliers may have some obvious distorting influence on the estimates produce by these techniques. This study investigates the distorting effect of outliers on four simultaneous equation estimation techniques through Monte Carlo method. Outliers of various degrees were introduced into observations of different sizes. The estimators were ranked based on their ability to absorb the shock due to outliers in the observations. The Total Absolute Bias (TAB), Variance and Root Mean Square Error (RMSE) were used in ranking the performances of the estimators. Based on the criterion of tab, two stages least squares (2SLS) ranked the best, closely followed by three stage least squares (3SLS) and ordinary least squares (OLS) in that order, while limited information maximum likelihood (LIML) was the poorest when outliers of not more than 5% are present in the observation. It is however, not strange to observe that OLS outperformed the other estimators when variance was used. This could be misleading since variance may be measured around a wrong parameter. Based on the criterion of RMSE, ordinary least squares yields estimates with the least value of RMSE while LIML yields the greatest when outliers of not more than 10% are present in the observation. Also it was established that OLS has the greatest capacity to absorb the shock due to the presence of outliers in the observation