Standard errors estimation in the presence of high leverage point and heteroscedastic errors in multiple linear regression

In this study, the Robust Heteroscedastic Consistent Covariance Matrix (RHCCM) was proposed in order to estimate standard errors of regression coefficients in the presence of high leverage points and heteroscedastic errors in multiple linear regression. Robust Heteroscedastic Consistent Covariance Matrix (RHCCM) is the combination of a robust method and Heteroscedasticit Consistent Covariance Matrix (HCCM). The robust method is used to eliminate the effect of high leverage points while HCCM is mainly used to eliminate the effect of heteroscedastic errors. The performance of RHCCM was assessed through an empirical study and compared with results obtained when the original Heteroscedastic Consistent Covariance Matrix was used

Human capital dynamics of regional growth in Nigeria: dynamic panel data approach

This paper investigates the human capital factors that contribute to the growth of the Nigerian regional growth rates. In particular, it is to determine whether the leading role of human capital factors in other economies could explain the regional growth processes dynamics in Nigeria. If these factors are not applicable, other possible explanations are to be identified for the country’s regional economic growth dynamics profile. Nigerian regional cross sectional data of financial, physical and human capital accumulation were utilized to run a growth accounting regression captured by an aggregate production function. The study uses panel data (cross sectional and time series data) from 1998 to 2008 and employed three (3) panel data models to estimate the dynamics and contribution of human capital factors. The results showed that the initial human capital stock has an influence on the GDP per capita growth rate. Similarly, the Southern regions that had a head start in school attendance have higher thresholds or secondary schools are significant; higher levels of schooling (secondary school) have significant impact on the GDP per capita growth rate. On the other hand, the Northern regions have lower technical frontier; as only the primary school have significant impact on the GDP per capita growth. However the federal financial allocation from the federation accounts was found to be significant across all regions with the exception of the South-South regions, and positive investment in physical assets of education. Thus it implies that regional differences should be taken into account when planning developmental processes

Technological Persuasive Pedagogy: A New Way to Persuade Students in the Computer-based Mathematics Learning

This study is an attempt to introduce a new technological pedagogy which is more effective in students attitudes toward mathematics. Content Analysis method as a qualitative research method was used in this research. Based on research method 16 principles were obtained prior persuasive models, theories and approaches. They are as usable principles to persuade students in the computer based mathematics classrooms. These principles can be employed by teachers and course ware designer in three different conditions; first, for students with negative attitude toward mathematics. Second, increase the positive attitude of the students to a higher level. Last but not least, for create a condition to prevent of changes in students' attitude from positive level to lower level. Keywords: Persuasion, Pedagogy, Persuasive Technology, Attitude, Computer-based Learnin

A hybrid model for improving Malaysian gold forecast accuracy

A hybrid model has been considered an effective way to improve forecast accuracy. This paper proposes the hybrid model of the linear autoregressive moving average (ARIMA) and the non-linear generalized autoregressive conditional heteroscedasticity (GARCH) in modeling and forecasting. Malaysian gold price is used to present the development of the hybrid model. The goodness of fit of the model is measured using Akaike information criteria (AIC) while the forecasting performance is assessed using bias, variance proportion, covariance proportion and mean absolute percentage error (MAPE)

The relationship between web 2.0 technologies and students achievement in virtual university

This paper has been investigated the effectiveness of Web 2.0 technology in virtual universities. Web 2.0 tools refer to the Web-based applications that allow virtual students to collaborate, communicate, and share information in a virtual or online learning environment. The population has been virtual students in developed and developing countries that based on Krejcie and Morgans' table, 384 students have been selected as sample. The results show that there is relationship between the use of Wikis, Podcasts, Blogs, and Web 2.0 technologies and students achievement in virtual university. Also using the Web 2.0 technology creates changes in communication, learning strategy, teaching methods, and interaction between learners and instructors. In virtual university many Web 2.0 tools contain characteristics of social software that maintain the ability to connect users and allow users to create Web content through collaborative efforts. Wikis, podcasts, and blogs represent social software that allows learner to collaborate by exchanging information through the Internet. Interaction and collaboration encourage learners to construct their knowledge, which remains characteristic of a constructivist approach to learning

E-learning and social media motivation factor model

The aims of this study are to probe into the motivational factors toward the usage of e-learning and social media among educational technology postgraduate students in the Faculty of Education, Universiti Teknologi Malaysia. This study had involved 70 respondents via the means of a questionnaire. Four factors have been studied, named, the factor of technology, exposure, content and social influence. Via Structural Equation Modeling (SEM), this research uncovers that respondents usage of e-learning is being motivated by the factor of technology and content. The respondents use of social media was found to be motivated by the factor of technology and social influence. A strong positive relationship exists between the usage of e-learning and social media suggesting that social media can be manipulated as supporting material for e-learning. Yet, the finding may not be generalized to all Malaysian educational technology postgraduate students

Univariate and multivariate GARCH models applied to the CARBS indices

Abstract: The purpose of this paper is to estimate the calibrated parameters of different univariate and multivariate GARCH family models. It is unrealistic to assume that volatility of financial returns is constant. In the empirical analysis, the symmetric GARCH, and asymmetric GJR-GARCH and EGARCH models were estimated for the CARBS indices and a global minimum variance portfolio (GMVP), the best fitting model was determined using the AIC and BIC. The asymmetric terms of the GJR-GARCH and EGARCH models indicate signs of the leverage effect. The information criterion suggest that the EGARCH model is the best fitting model for the CARBS indices and the GMVP

A Comparative Study On Some Methods For Handling Multicollinearity Problems

In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. A frequent obstacle is that several of the explanatory variables will vary in rather similar ways. As a result, their collective power of explanation is considerably less than the sum of their individual powers. This phenomenon called multicollinearity, is a common problem in regression analysis. Handling multicollinearity problem in regression analysis is important because least squares estimations assume that predictor variables are not correlated with each other. The performances of ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLSR) in handling multicollinearity problem in simulated data sets are compared to help and give future researchers a comprehensive view about the best procedure to handle multicollinearity problems. PCR is a combination of principal component analysis (PCA) and ordinary least squares regression (OLS) while PLSR is an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. RR on the other hand is the modified least square method that allows a biased but more precise estimator. The algorithm is described and for the purpose of comparing the three methods, simulated data sets where the number of cases were less than the number of observations used. The goal was to develop a linear equation that relates all the predictor variables to a response variable. For comparison purposes, mean square errors (MSE) were calculated. A Monte Carlo simulation study was used to evaluate the effectiveness of these three procedures. The analysis including all simulations and calculations were done using statistical package S-Plus 2000 software

A goal programming approach for the problems analyzed using the method of least squares

Goal programming (GP) is one of the most promising techniques for multiple objective decision analysis. Goal programming is a powerful tool which draws upon the highly developed and tested technique of linear programming, but provides a simultaneous solutions between variables, particularly for the purpose of understanding how one variable depend on one or more other variables. However, one of the main problems is that the method of least squares is biased by extreme cases. This study proposes goal programming as an alternative to analyze such problems. The analysis were done by using QM for Windows and MINITAB software package

Forecasting Malaysia load using a hybrid model

A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feed-forward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load performs better than both single models with mean absolute percentage error (MAPE) of less than 1%