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Subject: search.filters.subject.Ordinary least squares

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    Bootstrap approach for estimating seemingly unrelated regressions with varying degrees of autocorrelated disturbances
    (2013) Ebukuyo, O. B.; Adepoju, A. A.; Olamide, E. I.
    The Seemingly Unrelated Regressions (SUR) model proposed in 1962 by Arnold Zellner has gained a wide acceptability and its practical use is enormous. In this research, two methods of estimation techniques were examined in the presence of varying degrees of _rst order Autoregressive [AR(1)] coefficients in the error terms of the model. Data was simulated using bootstrapping approach for sample sizes of 20, 50, 100, 500 and 1000. Performances of Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) estimators were examined under a definite form of the variance-covariance matrix used for estimation in all the sample sizes considered. The results revealed that the GLS estimator was efficient both in small and large sample sizes. Comparative performances of the estimators were studied with 0.3 and 0.5 as assumed coefficients of AR(1) in the first and second regressions and these coefficients were further interchanged for each regression equation, it was deduced that standard errors of the parameters decreased with increase in the coefficients of AR(1) for both estimators with the SUR estimator performing better as sample size increased. Examining the performances of the SUR estimator with varying degrees of AR(1) using Mean Square Error (MSE), the SUR estimator performed better with autocorrelation coefficient of 0.3 than that of 0.5 in both regression equations with best MSE obtained to be 0.8185 using _ = 0:3 in the second regression equation for sample size of 50. Key words: Autocorrelation||Bootstrapping||Generalized least squares||Ordinary least squares||Seemingly unrelated regressions
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    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. T
    This 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
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    Comparative analysis of rainfall prediction using statistical neural network and classical linear regression model
    (Medwell Journals, 2011) Udomboso, C. G.; Amahia., G. N.
    Different types of models have been used in modeling rainfall. Since 1990s however, interest has shifted from traditional models to ANN in rainfall modeling. Many researchers found out that the ANN performed better than such traditional models. In this study, we compared a traditional linear model and ANN in the modeling of rainfall in Ibadan, Nigeria. Ibadan is a city in West Africa, located in the tropical rainforest zone, using the data obtained from the Nigeria Meteorological (NIMET) station. Three variables were considered in this study rainfall, temperature and humidity. In selecting between the two models, we concentrated on the choice of adjusted R2 (R-2 ), Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC). Though, the MSE and R2 were also used, it was concluded from results that MSE is not a good choice for model selection. This is due to the nature of the rainfall data (which has wide variations). It was found that the Statistical Neural Network (SNN), generally performed better than the traditional (OLS).