Seemingly Unrelated Regression Equations Research Articles

The Euro Area 12: A Comparative Assessment of Its Member States in the Period 1998–2022

The primary objectives of this research are to compare the economic performance of the Member States (MS) in the Euro Area 12 (EA-12) of the European Union (EU) that served as net contributors and net recipients of its budget during the period 1998–2022. The comparison focuses on aspects related to economic growth and the business cycle, exploring the presence of hysteresis. To achieve these objectives, a novel approach is employed, which integrates the analysis of both growth and cycles. This approach involves using the Seemingly Unrelated Regression Equations (SURE) model to estimate a translog production frontier and assess the impact of lagging output gaps on the potential output or capacity for each of the MS considered. The data used to estimate each frontier and analyze growth decomposition consist of quarterly data filtered using the Kalman filter. The results reveal that lagging output gaps, occurring one or two quarters prior, significantly affect the potential output or capacity of the EA-12 MS, thereby supporting the existence of hysteresis. However, the impacts appear to be minimal. Furthermore, the research concludes that changes in total factor productivity (TFP), mainly due to technical change, play a more crucial role in the growth of MS that are net contributors to the EU budget. Conversely, total factor accumulation (TFA), mainly physical capital accumulation, has a more pronounced impact on the growth of MS net recipients from the EU budget. Interestingly, the heterogeneity observed among the EA-12 MS extends beyond the simple division between net contributors and net recipients of the EU budget. This heterogeneity raises questions about the European Central Bank’s (ECB) ability to stabilize the economies of these various MS solely through conventional monetary policy.

Evaluating of the Restructuring of the Jordanian Electric Company (1968-2017)

Objectives: This study aims to identify the impact of the restructuring of Jordan's electricity sector on the advantages of vertical integration of the Jordanian Electric Power Company (JEPCO) before restructuring, experimentally verifying the existence of economies of scale and economies of scale (vertical and horizontal integration), and testing the natural monopoly of JEPCO using a time series during the period (2017-1968).
 Methods: To achieve the purposes of the study, a multistage and output quadratic cost function was estimated with equations of capital price contribution and work from total costs as a system of equations using the Seemingly Unrelated Regression (SUR) equation system.
 Results: Empirical results indicated real economies of scale, a particular product's economies of scale in both household and generating energy sales and non-household. The results also indicated savings in the range and vertical and horizontal integration, that the JEPCO had lost the vertical integration benefits of the company before it stopped generating electricity, and that the company's losses due to the vertical dismantling averaged 12.98 million dinars.
 Conclusions: Based on these results, the study emphasized the need to balance the cost of separating the activities that may occur as a result of losing the advantages of vertical integration with the expected benefits of paving the way for competition in the electricity generation activity, which in turn enhances cost savings for consumers.

A Monte Carlo study on the ridge parameter of the seemingly unrelated ridge regression models

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.

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

This study evaluates the effect of renewable, non-renewable energy consumption and economic growth on unemployment rate across 51 US states over the period 1977–2017. We applied a fixed effect model and a Seemingly Unrelated Regression Equations (SURE) model, which allows for an unknown form of cross-sectional dependence and slope heterogeneity. While the results of the fixed effects model indicate the negative and positive effects of non-renewable and renewable energy consumption on the unemployment rate, respectively, mixed results were received by employing SURE model at the state level. By allowing for slope heterogeneity, the results of the SURE model confirm that the non-renewable and renewable energy consumption only have job-creating effects in 19 and 6 out of 51 states, respectively. The renewable energy consumption has pro-unemployment effect in 20 states.

Selecting the covariance structure for the seemingly unrelated regression models

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.

Determinants of the risk perception of farmer–herder conflicts: evidence from rural Nigeria

PurposeThe purpose of this paper is to examine the social, economic and demographic determinants of rural households' risk perception of farmer–herder (FH) conflicts in Nigeria. The paper also investigates two aspects of FH conflict risk perception relating to food production and physical insecurity.Design/methodology/approachA FH conflict risk perception model is constructed and tested using exploratory factor analysis (EFA), ordinary least squares (OLS) and seemingly unrelated regression equation (SURE) models. The study uses cross-sectional data from 401 rural households in Nigeria.FindingsResults show that in addition to economic determinants like farm size, land ownership and crop diversity, socio-demographic variables like age and number of languages spoken are significant predictors of household risk perception of FH conflict. Second, although gender and frequency of FH conflict have no significant effect on the risk perception of FH conflict, there is a significant moderating effect of frequency of FH conflicts on the influence of gender on the risk perception of FH conflict. Third, findings also highlight the important predictors of the risk perception of FH conflicts relating to food production and physical insecurity.Originality/valueFindings give insight into policies targeted at influencing the risk behaviour of rural households. This is important to aid the development of efficient risk management initiatives.

Expenditures on sugar-sweetened beverages in Jamaica and its association with household budget allocation

BackgroundSugar-sweetened beverages (SSB) consumption is associated with overweight and obesity, which are important drivers for the increasing healthcare and other social costs. If expenditures on SSB decrease expenditures on other goods and services, such as education and healthcare, this “crowding-out” may have a lasting effect. The main objectives of this article are, first, to estimate the statistical association between the decision of spending in SSB and several households’ sociodemographic characteristics; and second, to estimate the association between the decision of buying SSB and budget allocation across categories in Jamaica.MethodsUsing the Jamaican Household Expenditure Survey 2004–2005 a generalized ordered probit model was estimated to examine the association between socioeconomic variables and the decision to spend on SSB. Seemingly Unrelated Regression Equations (SURE) of all the expenditure groups (except the SSB group) were used to estimate the association between the decision of buying SSB and budget allocation on other goods and services.ResultsExpenditures on SSB are negatively affected by the size of the household and the area of residence (rural households spend more on SSB than urban ones), while having a larger proportion of children (15 or younger) and having a larger total budget is associated to more expenditures on SSB. Households with positive expenditure on SSB allocate significantly less budget to “Healthcare” and “Education”, when compared to those who did not buy SSB.ConclusionsSSB expenditures may displace expenditures in necessary goods and services, which implies that decreasing the proportion of budget spent on SSB may have important present and future consequences on poorer households’ human capital accumulation and future incomes.

Expectation-Maximization Algorithm Estimation Method in Automated Model Selection Procedure for Seemingly Unrelated Regression Equations Models

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.

Robust SURE estimates of profitability in the Egyptian insurance market

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.

Contemporaneous or causal? Evaluating the triumvirate of insolvency risk, capitalization and efficiency in Indian commercial banking

PurposeThis paper empirically examines the short-term and long-term associations between risk, capital and efficiency (R-C-E) in the Indian banking sector across 2008–2019 to answer the presence of causation or contemporaneousness in the R-C-E nexus.Design/methodology/approachThe paper focuses on three objectives. First, the authors determine short-term causality in the risk–efficiency relationship by studying the simultaneous influence of a wide array of banking risks on DEA-based technical and cost efficiency in static and dynamic situations. Second, the authors introduce bank capital and contemporaneously determine the interplay between R-C-E using seemingly unrelated regression equation (SURE) and three-staged least squares (3SLS). Last, the authors assess stability in inter-temporal associations using Granger causality in an autoregressive distributed lag (ARDL) generalized method of moments (GMM) framework.FindingsThe authors contend that high capital buffers reduce insolvency risk and increase bank stability. Technically efficient banks carry lesser equity buffers, suggesting a trade-off between capital and efficiency. However, capitalization makes banks more technically efficient but not cost-efficient, implying that over-capitalization creates cost inefficiencies, which, in line with the cost skimping hypothesis, forces banks to undertake risk. Concerning causal relationships, the authors conclude that inefficiency Granger-causes insolvency and increases bank risk. Further, steady increases in capital precede technical and cost efficiency improvements. The converse also holds as more efficient banks depict temporal increases in capitalization levels.Originality/valueThe paper is perhaps the first that acknowledges the influence of the “time” perspective on the R-C-E nexus in an emerging economy and advocates that prudential regulations must focus on short-term and long-term intricacies among the triumvirate to foster a stable banking environment.

The effects of tourism on housing prices: applying a difference-in-differences methodology to the Portuguese market

PurposeThis paper aims to explore the effects of a surge in tourism short-term rentals (STR) on housing prices in municipalities within Portugal’s two largest Metropolitan Statistical Areas.Design/methodology/approachThis study applies the difference-in-differences (DiD) methodology by using a feasible generalized least squares (FGLS) estimator in a seemingly unrelated regression (SUR) equation model.FindingsThe results show that the liberalization of STR had a significant impact on housing prices in municipalities where a higher percentage of housing was transferred to tourism. This transfer led to a leftward shift in the housing supply and a consequent increase in housing prices. These price increases are much higher than those found in previous studies on the same subject. The authors also found that municipalities with more STR had low housing elasticities, which indicates that adjustments to the transfer of real estate from housing to tourism were made by increasing house prices, and not by increasing supply quantities.Practical implicationsThe study suggests that an unforeseen consequence of allowing property owners to transfer the use of real estate from housing to other services (namely, tourism) was extreme housing price increases due to inelastic housing supply.Originality/valueThis is the first time that the DiD methodology has been applied in real estate markets using FGLS in a SUR equation model and the authors show that it produces more precise estimates than the baseline OLS FE. The authors also find evidence of a supply shock provoked by STR.

The dynamics of Indian energy mix: a two-phase analysis

PurposeThe Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.Design/methodology/approachThe authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.FindingsEfficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.Originality/valueThe study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.

Impact of the EU Blue Card programme on cultural participation and subjective well-being of migrants in Germany.

The first aim of this study is to investigate the role of the EU Blue Card programme implemented in 2012 in Germany. In particular, we aim to explore the impact on the participation in cultural activities of first-generation non-European Union (EU) and non-European Economic Area (EEA) migrants, such as attendance to cinema, concerts and theatre. The second aim is to examine the impact of cultural activities on subjective well-being (SWB), measured by life satisfaction. We compare the cultural participation and life satisfaction between the treatment group that is the non-EU/EEA first-generation immigrants and the control group that consists, not only of natives and second-generation immigrants but also composes of EU/EEA first-generation immigrants who are not eligible to the programme. We will apply a sharp and a fuzzy regression discontinuity design (RDD) within a seemingly unrelated regression equations (SURE) system using the Ordered Probit method. The empirical analysis relies on data from the German Socio-Economic Panel (GSOEP) survey over the period 2015–2018. The results show that the treated subjects experience an increase in cultural participation activities and an improvement in their SWB, as a result of the EU Blue Card programme, compared to the control group. Participation in classical music performance, opera or theatre influences more the SWB compared to other cultural activities. Policies that promote labour market integration and participation in cultural activities will enable immigrants to integrate into the social norms of the host societies and improve their SWB.

Investigating the Heterogeneity of Economic Convergence in Latin America Countries – An Econometric Analysis of Systems of Regression Equations

For a selected group of Latin America countries we estimated the parameters of convergence equations on the basis of annual data. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE) that departures from standard approach utilizing panel regressions. We show empirical evidence in favor of the variability of parameters describing the convergence effect and productivity growth rates across analyzed club of countries. We also test several restrictions leading to less parameterized models imposing constancy of parameters of interest across countries.

Linking geospatial crash and citation data to inform equitable enforcement decisions

Risky and aggressive driving behaviors remain a serious safety concern across the globe. While engineering, education, and emergency medical services can affect the outcome of these behaviors, enforcement is a critical element of a well-rounded safety focus and has been proven to significantly reduce serious injury and fatal crashes. To appropriately and equitably allocate enforcement resources within a region, agencies must understand the relationship of crash locations (e.g., opportunities for improvement) and citation data (e.g., utilization of existing resources). To investigate this relationship, a case study approach was used to develop seemingly unrelated regression equation (SURE) models based on community attributes to predict crash and citation rates in the selected region. Through their application, regions that were overrepresented and underrepresented in their citation and crash rates were revealed. A geographic information system was used to illustrate these relationships using a combination of preliminary geospatial maps and maps based on the applied regression models. The resulting analysis revealed explanatory maps demonstrating potential enforcement gaps. The factors that most influence crash and citation rates were also revealed. This research provides a foundation for officials to improve the allocation of enforcement-related resources to equitably improve safety within a region.

Impact of Out of Pocket Health Expenditure on Consumption Pattern of Below Poverty Line Households in India

Financial burden from health expenditure is a serious cause of concern for low-income and developing countries. Studies have shown that low public investment in health, lack of adequate safety net mechanisms and poor quality of public health system in these countries are few basic grounds for placing high financial burden on people. In order to make both ends meet during illness, poor people resort to various alternative means. One of the popular strategies adopted is to reduce or reallocate the consumption bundle, so as to meet the medical expenditure. The impact of out of pocket (OOP) health expenditure on the consumer expenditure has immediate and direct impact especially among the poor sections of the society. This article aims to study the impact of out of pocket health expenditure on the constituents of consumer expenditure and how the composition of consumer expenditure differs with the levels of out of pocket expenditure among different consumption quintiles. The study uses IHDS household level survey data for the year 2011–2012. A mean comparison test is carried out to check whether significant difference exists in the consumption bundles of households with and without medical expenditure. A system of Engel curves is estimated with seemingly unrelated regression equation (SURE) to study the impact of out of pocket medical expenses on the consumption bundle. The results show that there exists significant difference in the share of different consumption items in the consumption bundles of households with and without out of pocket expenditure. Despite having larger share for food expenditure, poor households tend to protect the consumption of food share in the bundle. Compensation for rise in medical expenses is reflected in the reduced share of non-food expenditure items like fuel, education, entertainment, clothing and footwear. With high share of out of pocket expenditure, poor households in the states with high public health expenditure mainly reduced their education expenditure whereas consumption of most of the non-food items were reduced by poor households in low public health expenditure states.

Supply Response Analysis Of Indonesian Cocoa

The Supply response of cocoa in Indonesia is done through an approach to the area of cocoa plantations, in response to economic factors and non-economic factors. Data analyzed from 1985 to 2015 (time series). The aim is to find out the factors that affect the area of cocoa plantations in Indonesia and the response of the offer of cocoa in Indonesia seen from the value of short-term elasticity and long-term elasticity. The partial adjustment and adaptive expectation model was applied and continued with an analysis of Seemingly Unrelated Regression Equations (SURE). The results of the analysis show that the factors affect the area of cocoa plantations in Indonesia are on smallholder plantations, namely coffee prices, rubber prices, and technology. In the state plantations, namely the previous year's cocoa plantation area, rubber prices, technology, and government policy. In private plantations, namely rubber prices, technology, and labor costs. Supply response of cocoa that is in line with the value of elasticity both short and long term is inelastic which means that it is following expectations, where the value of long-term elasticity is greater than the value of short-term elasticity

Estimating asphalt mixture volumetric properties using seemingly unrelated regression equations approaches

As the asphalt pavement industry seeks to manufacture products with low variability, advances in modeling approaches that help optimize asphalt materials are lagging behind. Typically, well-controlled paving projects provide asphalt mixture volumetric properties with low variability, while poorly controlled projects result in higher variability in asphalt mixture volumetric properties. Additionally, on many occasions, the management of all the factors influencing asphalt mixture production and construction is inadequate. The work described in this paper demonstrates that seemingly unrelated regression equations (SURE) approaches can be used to estimate asphalt mixture volumetric properties using mixture design, material properties, and testing inputs. SURE approaches can help evaluate asphalt mixture production and placement by accounting for the deficiencies and limitations in quality assurance data. Moreover, SURE approaches analyze asphalt mixtures in a concise, yet robust, manner. The findings of this study contribute to a better understanding of variability in asphalt material production and placement.

English

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

The heterogeneity of convergence in transition countries

For two groups of post-communist countries (CEE and CIS) we estimated the parameters of convergence equations on the basis of annual data. We depart from standard econometric theory, which involves panel regression techniques. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE). We show empirical evidence in favour of the variability of parameters describing the convergence effect and productivity growth rates across countries. Our approach seems a convincing alternative to the panel regression approach where random effects can be estimated, imposing an assumption about the constancy of structural parameters within the group of countries under analysis. We discuss the role of the global financial crisis in the heterogeneity of convergence processes and productivity at the country level. The aforementioned SURE model was estimated based on two datasets, one containing observations prior to the crisis and the second containing the whole sample.