Browsing by Author "Yahya, Waheed Babatunde"

Now showing 1 - 5 of 5
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    Modeling Effect of Some Factor that Contribute to Cereals Yields in Nigeria using Toda- Yamamoto Techniques
    (Sule Lamido University Journal of Science and Technology (SLUJST), 2020-06) Garba, Mohammed; Akanni, Saheed B; Yahya, Waheed Babatunde; Kareem, K. Y.; Afolayan, Razaq
    This study aimed to examine the direction of causality among Cereals Production (CP), Land used for Cereals Production (LP) and Cereal Yields (CY) in Nigeria for a period of 50 years (1966 to 2016) using techniques of Toda-Yamamoto. The maximum order of integration and optimal lag order of the series confirmed that VAR(2+1) model best fitted the data. Results from the estimated model revealed that two year past values t-2 (2014 and 2015) of CP is the major determinant of CP series in current time period t (2016) while one year past value t-1 (2015) of CY and two year past values t-2 (2014 and 2015) of CP and LP are major determinants of CY series in current time period t (2016).The results of Toda-Yamamoto causality examination showed that CY is Granger caused by both CP and LP. Based on the sequence of analyses carried out in this study, it was concluded that cereals yields in Nigeria can be predicted by both cereal production and the size of farmland used for planting cereal crops. The study then recommended that adequate plots of land be allocated to farmers interested in cereals production in order to improve yields of cereals and ensure food security in the country.
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    Modeling Nigerian Electricity Generation and Consumption Pattern
    (Journal of Science, Technology and Mathematics Education (JOSTMED), Federal University of Technology, Minna, 2015-03-02) Garba, Mohammed Kabir; Ajao, Kajogbola Razaq; Yahya, Waheed Babatunde; Oyeyemi, Gafar Matanmi
    This study examined annual amount of electricity generated and consumed in Nigeria for the period spanning 1970 to 2012. The Box-Jenkins modeling approach was employed after the series were transformed to ensure stationarity using the first differencing method. The empirical results showed that ARIMA (1, 1, 0) and ARIMA (0, 1, 1) models fitted the electricity generation and consumption datasets adequately. The whiteness of the residuals from the models was verified using Ljung-Box methodology. The projections for both electricity generation and consumption for five years ahead were made with 80% and 95% confidence limits.
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    Modeling Panel Data in The Presence of Autocorrelation, Heteroscedasticity and Collinearity: A Monte Carlo Study
    (Library and Publications Committee, University of Ilorin, Nigeria, 2015-06-04) Garba, Mohammed Kabir; Yahya, Waheed Babatunde; Oyejola, Benjamin Agboola
    In this work, panel data that were characterized by features of autocorrelation, heteroscedasticity and collinearity were modelled using four estimation methods: Pooling (OLS), First-Differenced (FD), Between (BTW) and Feasible Generalized Least Squares (FGLS). Panel data like other aspects of econometrics, exploits regression analysis as one of the statistical tools to formulate, illustrate and appraise models. The regression analysis requires some assumptions which, if violated, results to one problem or the other. In such situation, the Pooling method of estimation which is the naive approach remains linear, unbiased and asymptotically normally distributed but might not be efficient as the estimates of the parameters might become indeterminate, the associated confidence intervals may be too wide and the standard errors might become infinitely large. Monte-Carlo studies were carried out at different sample sizes and time periods, varying degrees of heteroscedasticity and levels of autocorrelation and collinearity. The results from this work showed that in small sample situations, irrespective of number of time periods, FGLS is preferable when heteroscedasticity is severe regardless of levels of autocorrelation and multicollinearity. But when heteroscedasticity is low or mild with moderate autocorrelation level, both FD and FGLS are preferred, while BTW performs better only when there is no autocorrelation and low degree of heteroscedasticity. However, in large samples with little time periods, both FD and BTW could be used when there is no autocorrelation and low degree of heteroscedasticity, while FGLS is preferred if otherwise.
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    On Seemingly Unrelated Regression and Single Equation Estimators under Heteroscedastic Error and Non-Gaussian Responses.
    (FUOYE Journal of Engineering and Technology, 2020) Afolayan, Razaq Bayo; Banjoko, Alabi Waheed; Garba, Mohammed Kabir; Yahya, Waheed Babatunde
    - This study investigated the efficiency of Seemingly Unrelated Regression (SUR) estimator of Feasible Generalized Least Square(FGLS) compared to robust MM-BISQ, M-Huber, and Ordinary Least Squares (OLS) estimators when the variances of the error terms are non-constant and the distribution of the response variables is not Gaussian. The finite properties and relative performance of these other estimators to OLS were examined under four forms of heteroscedasticity of the error terms, levels of Contemporaneous Correlation (Cc) with gamma responses. The efficiency of four estimation techniques for the SUR model was examined using the Root Mean Square Error (RMSE) criterion to determine the best estimator(s) under different conditions at various sample sizes. The simulation results revealed that the SUR estimator (FGLS) showed superior performance in the small sample situations when the contemporaneous correlation (ρ) is almost perfect (ρ=0.95) with the gamma response model while MM-BISQ was the best under low contemporaneous correlation. The relative efficiencies of MM-BISQ, M-Huber and FGLS estimators over the OLS are respectively 89%, 71%, and 14% in a small sample (𝑛 ≤ 30) and 49%, 32% and 1% in large sample sizes (𝑛 > 30) under gamma response model. The study concluded that MM-BISQ and M-Huber estimators are the most efficient estimators for modeling systems of simultaneous equations with non-Gaussian responses under either homoscedastic or multiplicative heteroscedastic error terms irrespective of the sample size.
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    On the use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company
    (Islamic University Multidisciplinary Journal (IUMJ), Uganda, 2020-04-25) Garba, Mohammed Kabir; Banjoko, Alabi Waheed; Yahya, Waheed Babatunde; Gatta, Nusirat Funmilayo
    This study employed the utilization of a slack starting solution approach of the Simplex Optimization method to build a Linear Programming (LP) model. Data were extracted from the record unit for an item blend fabricating industry, Fortunate Bakery, Ilorin, Nigeria. The information was collected on four noteworthy sorts of bread produced by the bakery which includes; Special Delight (SD), Ordinary Slice Bread (OSB), Ordinary Gala (OG) and Saloon (S) regarding their unit costs and selling prices, the raw materials utilized and the amount of each of the crude materials held in stock every day. In view of the data supplied, a linear programming problem was developed with the goal of maximizing the daily profit of the organization. The optimal daily profit that would be achievable to the organization in the item blend was resolved utilizing the methodology specified earlier. The results showed that the organization would achieve ideal daily profit level of ₦ 9,500 (or monthly profit level of ₦285,000) if she concentrates on the production of type alone is given to the unit offers of Saloon bread and disregard other lines of items produced by the company. By this, aggregate daily offers of about 380 (or monthly offers of about 11,400) units of Saloon bread would be sold by the organization. The data analyses were carried out USING Tora software package.