How do Japanese households juggle energy sources? A deep dive into price responses

An alternative approach for the numerical solution of seemingly unrelated regression equations models

This study investigates the factors influencing academic performance of students at Modibbo Adama University of Technology, Yola, Adamawa State, using the Seemingly Unrelated Regression Equations (SURE) model. Secondary data were collected from the Department of Statistics and Operations Research, encompassing variables including age at entry, gender, mode of entry, school type, parent occupation, course scores from 100 to 500 level, and CGPA for each session across three student cohorts (2016, 2017, and 2018). The SURE model was employed to analyze three dependent variables: first year CGPA, final year CGPA, and total credit units passed, while accounting for contemporaneous correlation among the error terms. Model selection was conducted using log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The SURE model demonstrated superior performance over multivariate regression, with lower root mean square error values (0.6291, 0.5596, and 3.1884 for first year CGPA, final year CGPA, and total credit passed, respectively). The model explained 55.6%, 60.1%, and 96.0% of the variance in first year CGPA, final year CGPA, and total credit units passed, respectively. JAMB score emerged as the most significant predictor across all three dependent variables (p < 0.001), while program type significantly affected all performance measures at the 5% level. Age significantly influenced total credit units passed, and student set significantly affected first year CGPA. Conversely, gender, mode of entry, and number of O-level sittings showed no significant effects on any performance measure. The SURE model effectively captures the interdependencies among different academic performance indicators, with JAMB score serving as the strongest predictor of student success. The findings support the continued use of University Tertiary Matriculation Examination (UTME) scores as a reliable admission criterion for academic programs.

ObjectiveThis paper is concerned with evaluating suggested approach of selecting the suitable covariance structure for fitting the seemingly unrelated regression equations (SURE) models efficiently. MethodThe paper assessed AL-Marshadi (2014) methodology in terms of its percentage of times that it identifies the right covariance structure for mixed model analysis of SURE models using simulated data. ApplicationThe simulated equations of SURE models have identical explanatory variables, the regressors in one block of equations are a subset of those in another, and different regressors in the equations with various settings of covariance structures of ∑. Moreover, the percentage of times that REML fail to converge under normal situation are reported. The application of the proposed methodology is given using a panel of data. ConclusionsIn short, AL-Marshadi (2014) methodology provided an excellent tool for selecting the right covariance structure for SURE models using restricted maximum likelihood (REML) estimation method in order to fit the SURE models more efficiently than the existing method that considering the stander unstructured covariance structure in fitting SURE models.

This paper proposes three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

Model selection is the process of choosing a model from a set of possible models. The model's ability to generalise means it can fit both current and future data. Despite numerous emergences of procedures in selecting models automatically, there has been a lack of studies on procedures in selecting multiple equations models, particularly seemingly unrelated regression equations (SURE) models. Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. This extension procedure was originally initiated from Autometrics, which is only applicable for a single equation. To assess the performance of SURE(EM)-Autometrics, simulation analysis was conducted under two strengths of correlation among equations and two levels of significance for a two-equation model with up to 18 variables in the initial general unrestricted model (GUM). Three econometric models have been utilised as a testbed for true specification search. The results were divided into four categories where a tight significance level of 1% had contributed a high percentage of all equations in the model containing variables precisely comparable to the true specifications. Then, an empirical comparison of four model selection techniques was conducted using water quality index (WQI) data. System selection to select all equations in the model simultaneously proved to be more efficient than single equation selection. SURE(EM)-Autometrics dominated the comparison by being at the top of the rankings for most of the error measures. Hence, the integration of EM algorithm estimation is appropriate in improving the performance of automated model selection procedures for multiple equations models.

Renewable energy sources and unemployment rate: Evidence from the US states

In this paper we examine the application of the Least Absolute Deviations (LAD) method for ridge-type parameter estimation of Seemingly Unrelated Regression Equations (SURE) models. The methodology is aimed to deal with the SURE models with non-Gaussian error terms and highly collinear predictors in each equation. Some biasing parameters used in the literature are taken and the efficiency of both Least Squares (LS) ridge estimation and the LAD ridge estimation of the SURE models, through the Mean Squared Error (MSE) of parameter estimators, is evaluated.

The ridge regression approach can also be applied to deal with multicollinearity in the seemingly unrelated regression equations (SURE) models. However, to get the ridge-type estimators of the coefficients in a SURE model, choosing the ridge parameter has always been an important and challenging task. Herein, to cope with this issue, an optimal choice of the ridge parameter is proposed based on the generalized cross-validation (GCV) criterion. Moreover, some existing estimators of this parameter, in the context of the ordinary ridge regression models, are extended to be used in the SURE models. All these estimators, including the proposed and extended ones, and several state-of-the-art alternatives (altogether 36 different estimators) are compared with each other in terms of the GCV criterion. Lastly, an application of the methodology is given on chronic renal failure effect data.

The price and output elasticities of energy demand continue to be of interest to academia and policy institutions, having been estimated in previous studies. However, the estimated results show some inconsistencies, especially at the sectoral level, across countries. Based on our conjecture that those inconsistencies are mainly due to the effect of contingent energy intensities and partially to different units of analysis, we narrowed the analysis to the industry level and classified 16 industries into energy-intensive and less energy-intensive groups. The effects of price and output on energy demand were then compared between these two groups using 274 industry panel data across 20 Organization for Economic Cooperation and Development (OECD) countries from 1978 to 2013. The results showed that the price elasticity of energy demand was consistently lower in the energy-intensive group than in the less energy-intensive group, whereas the output elasticity of energy demand was higher in the energy-intensive group than in the less energy-intensive group. Using panel differences and system generalized method of moments estimations, the dynamic elasticities of energy demand were also estimated. Energy demand in reaction to both price and output changes appeared to be more elastic in the long term than in the short term for both energy-intensive and less energy-intensive groups. These findings could be a useful reference for policy makers to deploy separate energy policies for different industries aiming for different temporal effects.

In this paper we introduce an interesting feature of the generalized least absolute deviations method for seemingly unrelated regression equations (SURE) models. Contrary to the collapse of generalized leasts-quares parameter estimations of SURE models to the ordinary least-squares estimations of the individual equations when the same regressors are common between all equations, the estimations of the proposed methodology are not identical to the least absolute deviations estimations of the individual equations. This is important since contrary to the least-squares methods, one can take advantage of efficiency gain due to cross-equation correlations even if the system includes the same regressors in each equation.

Factors affecting public transportation, car, and motorcycle usage

The study set out to evaluate the relationship between sectoral Aid for Trade (AfTS) and sectoral exports within East Africa – represented by the East African Community partner states including Burundi, Kenya, Rwanda, Tanzania and Uganda. The Estimation method used was the Seemingly Unrelated Regression Equation (SURE) model. The SURE estimation results show a positive significant relationship between AfTS and exports from the agriculture, manufacturing and services sectors in the East Africa Region, implying that the initiative has and continues to foster the growth of exports from the region. This relationship however is inelastic, implying that percentage increases in aid disbursed lead to smaller percentage increase in sectoral exports. The results also show a highly significant, positive and elastic relationship between value addition and exports. Other regressors like regulatory quality and corruption control also show a higher impact on exports than AFTS. This shows that while AfTS can contribute to improved export performance, improvements in value addition, the quality of the regulatory environment, and the level of corruption control are equally or even more important in facilitating export growth. From the correlation coefficients between the sectors, all the three sectors are positively correlated. It can also be seen that the greatest correlations exist between the manufacturing sector and the agriculture sector, which could be because the countries in the study –from East Africa are mainly agriculture exporters, with a lot of inputs feeding from the agriculture sectors to the manufacturing sectors. Key words: Aid for trade, sectoral exports, seemingly unrelated regression model. &nbsp

This paper investigates the relationship between medical treatment costs and the length of hospital stays resulting from motorcycle crashes involving the elderly. The World Health Organization defines ‘elderly’ as people more than 65 years old. The sample for this study consisted of data for the year 2007 collected by the Bureau of National Health Insurance, Taiwan. We develop models for predicting medical costs and the length of hospital stays based on diagnosis, hospital and user types. The seemingly unrelated regression equation (SURE) model was applied first to investigate the relationship between medical costs and the length of hospital stays. The SURE model shows that the type of injury (e.g. head injury) is statistically significant and has positive effects on medical costs for motorcycle crashes involving the elderly in Taiwan. Due to the statistical insignificance of the dependency between medical costs and length of hospital stays, two separate simple linear regression models were subsequently estimated. For motorcycle crashes, patients over 80 years old had the highest medical costs. The findings reinforce the need for transportation authorities to focus on preventing certain types of injuries that are particularly serious and costly for the elderly in Taiwan.

This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.

Efficiency properties of feasible generalized least squares estimators in SURE models under non-normal disturbances