Multi-equation Model Research Articles

Sustainable Development of the Energy Sector in the Context of Socioeconomic Cohesion in France, Germany, and Poland

The energy sector is crucial for the country’s stable and lasting socioeconomic development and for implementing environmentally friendly technologies. The sustainable development of the energy sector takes place in three pillars: economic, social, and environmental. It means improving economic results while increasing the share of alternative energy sources. The paper aims to assess the impact of socioeconomic cohesion on the sustainable development of the energy sector in France, Germany, and Poland. We created indicators of sustainable development and socioeconomic cohesion. We used correlation analysis to examine the relationships. Additionally, we created a single- and multi-equation model, which we reset using the ordinary least squares method and Seemingly Unrealized Regression. The results of the linear correlation coefficients show a very strong relationship between socio-economic cohesion and the sustainable development of the energy sector in Poland (Pearson’s R 0.94), a weaker correlation in Germany (0.811), and the weakest in France (0.59). The results suggest that the relationship is strongest in the country with the lowest economic development and the smallest share of renewable energy, a significant positive impact on the sustainable development of the energy sector. The strength of this impact is varied in the countries studied, indicating a different level of advancement and development in the countries studied.

Sectoral Composition of Outward FDI and its Impact on Export Diversification in Saudi Arabia

This research explores the relationship between outward foreign direct investment (FDI) and export diversification in emerging economies, with a focus on the case study of Saudi Arabia. Utilizing a multi-equation model and vector autoregression analysis, the study investigates various factors such as the level and sectoral composition of outward FDI, income levels, institutional factors, and regional trade agreements. The findings suggest that while outward FDI can influence export diversification, its impact varies based on factors such as the sectoral distribution of investments, income levels, and institutional quality. Additionally, cointegration and vector error correction models reveal long-term relationships among key variables, highlighting the complex interplay between economic development, governance, and investment patterns. Impulse response analysis sheds light on the dynamic nature of shocks and their effects on the economy over time, emphasizing the interconnectedness of variables. Variance decomposition analysis further elucidates the significance of shocks in explaining fluctuations in model variables, underscoring the systemic interdependencies at play. Overall, the study provides valuable insights into the mechanisms through which outward FDI influences export diversification and economic development in emerging economies like Saudi Arabia and institutional reforms are essential; strengthening institutions and improving regulatory frameworks can amplify the positive effects of outward FDI on export diversification.

Economic spillovers from cloud computing: evidence from OECD countries

ABSTRACT The objective of this paper is to estimate the impact of cloud computing on economic performance. The analysis relies on a multi-equation model in which cloud computing is complementary with broadband to maximize the economic contribution of digital technologies. The proposed model is estimated for a sample of OECD countries, depicting cloud penetration at the sector level. Our results suggest positive effects from cloud adoption on economic outcomes. However, the impact of cloud computing depends on broadband penetration and cloud adoption, which, as expected, vary by country and industry. The sector benefitting from the largest economic impact is information and communications, followed by manufacturing, while in contrast, no impact was detected for the transportation sector. This is one of the first empirical analysis estimating cloud economic spillovers on an aggregated basis, from which important public policy implications can be derived to stimulate the development and adoption of this technology.

THE IMPACT OF ENVIRONMENTAL TAXES ON TRANSPORTATION AND STORAGE ENTERPRISES’ DEVELOPMENT – THE CASE OF BALKAN COUNTRIES

Sustainable development counteracts climate change and strives for a good quality of life. It is a development based on economic, social and environmental goals which should be evaluated. Environmental taxes have to ensure enterprises’ development follows sustainability principles. The basis of environmental taxes is a physical unit of harmful substance emissions with a proven negative impact on the environment. The article’s main objective is to assess the impact of environmental taxes (Envtax) on the sustainable development of the transportation and storage enterprises (Susd) (NACE Rev. 2: H) from 2008 to 2018. We created integrated indicators of sustainable development and its components, single-equation and multi-equation models (the OLS estimation) and Model Autoregressive Moving Average with eXogenous (ARMAX) to verify the research hypothesis. The research results indicate that energy (Entax t), transport (Trtax) and pollution taxes (Poltax) from current and previous period significantly affect the development of transportation and storage enterprises within sustainability principles (economic: Ed, social: Sd, and environmental: Envd). There is a strong variation in the strength and direction of the impact of environmental taxes on the development of the transportation and storage enterprises following sustainability principles in Bulgaria, Croatia, Romania, and Slovenia from 2008 to 2018. The results indicate that taxes are not effectively used and that implementing reforms in the European Union in this area is the right direction.

Innovation marketing management by using negative emotional value and impact of online learning on intention to enrol

This study was purposed to determine whether online learning influences e-service quality at the university administration during the pandemic and its impact on intention to enrol, because all high school students have been learning from home for two years, so the interaction behavior has pushed the administration of the university to become more digital than before. The second problem is that before this research, most marketing approaches only focused on positive emotions to make a decision to enroll, and this research has proven that the negative emotion can be used by marketing to influence the student to enrol in the university. The methodology in this research is a quantitative methodology the partial least squares structural equation modeling (PLS-SEM) analysis method using the SmartPLS was applied. A multi-equation model, namely the outer model and inner model, was used. In the outer model, the sample consists of 242 students of tenth to twelfth grade in high school in Jakarta, Banten, Lampung, and Yogyakarta. The main finding and contribution from this research are that the online experience of students in higher education will influence the e-service quality at the university administration, so the student experience in online learning influence e-service quality to intention to enroll. It was found that negative emotion positively influences and is significant to the intention to enrol and this can be used for one of the marketing strategies. The limitation of this research is that the negative emotions in this research cover only nervousness, so further research can test other negative emotions that can affect the intention to enroll.

Numerical and experimental studies of packed bed thermal energy storage system based on a novel transient energy model

AbstractPacked bed thermal energy storage (PBTES) is an essential means to solve the temporal difference and continuity between energy supply and utilization in the fields of concentrating solar power, compressed‐air energy storage, and waste heat recovery. In this paper, to solve the imperfection and inaccuracy of the current energy model, a precise transient two‐dimensional multiequation model is developed. The model comprehensively considers thermal dispersion, wall effects, thermophysical property changes, and other factors. Experiments are performed to compare and validate the simulation results, and the simulation error is −4.5% to 1.9%, achieving excellent simulation accuracy. Also, the wall effect on the PBTES is observed, and the gas‐solid temperature fields exhibit an evident nonuniform oscillation distribution in the radial direction. The effects of storage tank length, thermophysical properties, particle diameter, and gas‐solid initial temperature on the heat release rate are discussed in detail. Results indicated that decreasing the tank length, increasing the gas‐solid temperature difference, and particle materials with high thermal diffusivities can increase the heat release rate, and a clear linear change was found. Particularly because a larger range of particle diameter variation is considered, the heat release rate is shown to vary nonlinearly with particle diameter, and the selection of the optimal diameter must comprehensively consider the effects of the heat capacity and the heat transfer rate. This study provides a theoretical basis for the design of thermal energy storage systems.

The Role of Broadband Infrastructure in Building Economic Resiliency in the United States during the COVID-19 Pandemic

The purpose of this paper is to study the role of broadband in mitigating the economic losses resulting from COVID-19 in the United States by providing a necessary infrastructure to keep economic systems operating, albeit partially. The study is based on an empirical framework underlined by a Cobb–Douglas production function and estimated within a structural multi-equation model through the three-stage least squares approach. To consider the impact of COVID-19 on the economy, we rely on two main variables: an indicator of the quantity of deaths attributed to the disease for every 100,000 inhabitants; and the Stringency Index, a metric linked to the intensity of social restrictions imposed by national and local governments. The main contribution of this article is to provide robust evidence for how the heterogeneous effects of the pandemic across states are in part explained by differences in broadband adoption. Our results indicate that those states with higher broadband adoption were able to mitigate a larger portion of their economic losses derived from the pandemic-induced lockdowns. Addressing the digital divide and ensuring universal access to broadband represent critical goals for building economic resilience to face future emergencies.

Well-being and Career Decision-making Difficulties Among Master’s Students: A Simultaneous Multi-Equation Modeling

Well-being and Career Decision-making Difficulties Among Master’s Students: A Simultaneous Multi-Equation Modeling

Purchase intention of frugal household appliances among generation y in Germany – modified tam approach

Frugal innovations are characterized by a focus on the core product functions, high product quality, and the reduction of raw material and financial resources across the entire value chain. They were initially developed for people in emerging countries and focused on the unmet needs of people at the bottom of the pyramid (BOP). Different researches show that frugal innovations could also be interesting for people in industrialized countries. Existing literature indicates that customers in developed countries have different acceptance factors for frugal innovations than customers in emerging countries. The present study refers to this gap. For the first research, potential users of Generation Y from Germany are used. This generation comprised a large consumer group of more than 15 million people. They are characterized by their interest in sustainable products and social fairness. Household appliances such as washing machines, dishwashers, or coffee machines are used as a potential product group for frugal innovation in developed countries like Germany. They have been an integral part of daily life in industrialized countries for many years, but they are expensive and often highly sophisticated with many functions. The basis for the study is the Technology Acceptance Model (TAM) expanded based on the literature. A total of 12 hypotheses were derived and combined into a study model. The subject of the research was Generation Y in Germany. For model testing, a dataset of 463 participants was generated through an online survey from December 2020 to January 2021. A multi-equation structural model was created and evaluated with the variance-based procedure PLS-SEM to analyze the relationships. The results confirmed that even in the case of frugal household appliances, the opinion of third parties influences the usefulness and the purchase intention in Generation Y. Furthermore, there was an influence of environmental awareness on the attitude towards use. Despite the affinity for technological applications, the ease of use of a frugal household appliance was shown to influence the usefulness of frugal household appliances. Contrary to expectations, the performance expectations of household appliances and the possible price advantage did not influence usefulness within this consumer group. This study concluded with indications for future research approaches in this research area.

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.

Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation.

Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates for different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting.

The Role of Greenhouses in Filling Trade Gap of Tomato Crop in Saudi Arabia

USING, time series data, this research aimed at studying the role of greenhouses planted with tomatoes in reducing the import trade gap in Saudi Arabia by forecasting the impact of the change in the area of greenhouses of tomatoes on imports and total production in the Kingdom of Saudi Arabia. The study adopted Piecewise Linear Regression, ordinary least square and simultaneous multi-equation models, using Seemingly Unrelated Regression model. The results of the study indicated that the area and production of tomatoes in greenhouses showed a decreasing trend during the study period. As a result, there was an import trade gap for tomato crops estimated at 171.1 thousand tons on average for the study period, which represents about 42.2% of the average total tomato production. The most important determinants of the production of tomatoes in greenhouses include the area of greenhouses planted with tomatoes, one year lagged tomato price, and the value of medium-term loans allocated to greenhouses production. The study showed that the effect of temperature change on tomato production is more significant than carbon dioxide emissions. The study estimated forecasted reduction in the Kingdom's imports of tomatoes by 98% if the area of greenhouses is doubled. Hence, to reduce trade deficit of tomatoes and convert the deficit into a trade surplus, the study recommended horizontal expansion in greenhouses planted with tomatoes.

The accuracy of alternative supervisory methodologies for the stress testing of credit risk

Following the financial crisis approaches such as regulatory capital ratios have been supplanted by stress testing as a primary tool for managing systemic risks. Financial institutions are mandated to perform stress testing to forecast performance over stress scenarios. While in parallel supervisors conduct their tress tests to set minimum regulatory capital, nothing is revealed regarding the accuracy of such models. We investigate a modelling framework rather close to that of the regulators, projecting financial statement line items for an aggregated 'average' bank. We assess the accuracy of alternative stress testing models, simple single equation versus complex multiple equation approaches. Results show inaccuracies in stress test model forecasts for multi-equation models that do not account for the dependency structure amongst the model variables. This highlights the public policy need for reconsidering the existent regulatory stress tests, and the need for supervisory models to be subject to model validation standards.

ENTREPRENEURIAL CAPITAL AND PRODUCTIVE EFFICIENCY: THE CASE OF THE SPANISH REGIONS

The aim of this paper is to analyse the interrelation between entrepreneurial capital and productive efficiency of the Spanish regions while verifying the importance that business dynamics and entrepreneurial activity have on regional economic growth. For this purpose, data are used from the seventeen Spanish regions during the period 2003−2016. A multi-equation model is estimated that enables the analysis, on the one hand, of the existence of a causal relationship between business dynamics and entrepreneurial activity and, on the other hand, of these variables on the total factor productivity. The results lead to the conclusion that the entrepreneurial activity and the net creation of companies have a positive effect on productive efficiency and can explain the differences in the economic growth of the regions. In addition, the stock of human capital, the promotion of innovation and the degree of technological innovation act as catalysts for the productive efficiency of the regions. Finally, the results verify the fulfilment of the Schumpeter effect by which entrepreneurial capital generates economic growth.

Periodically correlated models for short-term electricity load forecasting

Abstract During the last two decades, the model developed by Cancelo and Espasa (1991) has been used for predicting the Spanish electricity demand with good results. This paper proposes a new approach for estimating multiequation models that extends the previous work in different and important ways. Primarily, 24-h equations are assembled to form a periodic autoregressive-moving-average model, which significantly improves the short-term predictions. To reduce the computational problem, the full model is estimated in two steps, and a meticulous model of the nonlinear temperature effect is included using regression spline techniques. The method is currently being used by the Spanish Transmission System Operator (Red Electrica de Espana, REE) to make hourly forecasts of electricity demand from one to ten days ahead.

Heteroscedastic stratified two-way EC models of single equations and SUR systems

Abstract A relevant issue in panel data estimation is heteroscedasticity, which often occurs when the sample is large and individual units are of varying size. Furthermore, many of the available panel data sets are unbalanced in nature, because of attrition or accretion, and micro-econometric models applied to panel data are frequently multi-equation models. The general least squares estimation of the heteroscedastic stratified two-way error component (EC) models of both single equations and seemingly unrelated regressions (SUR) systems (with cross-equation restrictions) on unbalanced panel data is considered. The derived heteroscedastic estimators of both single equations and SUR systems improve the estimation efficiency.

Working while Studying: Employment Premium or Penalty for Youth in Benin?

Most youth in developing countries leave school with only a general academic education level, slowing down their transition to the labor market. We analyze whether work experience during school can help youth transition more easily to a first job in Benin. We used data from the 2014 School-to-Work Transition Survey (SWTS) and a multi-equation model to account for endogeneity and sample- selection bias in estimating the effect of work experience during school on the transition to first job. Our findings are that work during summer breaks or holidays makes the transition from school to first job easier, especially when combined with apprenticeships, but these results were significant only for men and youth who left school with at least a secondary education. The important impact of work experience during studies on the ability to pursue job opportunities after school is highlighted.

Receiving Social Support after Short-term Confinement: How Support Pre- and During-confinement Contribute

Objectives: To test the independent links between social support that exists prior to and during confinement with support after release for adult males incarcerated for an average of 11 months in the Netherlands. Methods: Longitudinal data from a large study on consequences of confinement, the Prison Project, are used to describe instrumental (live with) and expressive (core network) support before and after confinement from four sources (parent, partner, other family, friend) and during-confinement visits by the same groups. Multiequation models examine the contribution of preconfinement support and visits to postconfinement support, while also describing the interrelationship of support sources. Results: Preconfinement support is consistently related to receiving the same type after release. Receiving visits during confinement has a unique relationship with receiving postconfinement expressive support across all relational groups. Only visits from partners has an additional influence on instrumental support after release. Postconfinement support across provider groups is interrelated, with a positive correlation across providers for expressive support and a substitution effect for instrumental support between parents and partners. Conclusions: After controlling for important preconfinement differences in support, visits remain significantly related to postconfinement expressive support, suggesting a possible mechanism by which visits help improve reentry outcomes.

When consumers can return digital products: Influence of firm- and consumer-induced communication on the returns and profitability of news articles

The news industry is being massively disrupted by the digital distribution of news. Consequently, publishers have revised their business models and integrated pay-per-article options. To reduce pre-purchase uncertainty, consumers can use information from firm-induced (e.g., newsletters), or consumer-induced communication (e.g., likes). These communication activities avoid purchases with poor fit but also increase customer expectations. Consequently, their effect on sales, returns, and profitability is unclear. For digital products, these effects are even less clear because product quality is difficult to evaluate pre-purchase, and products can be returned at almost no cost, even after consumption. In this study, we investigate the effects of firm- and consumer-induced communication on digital returns in the context of news articles on a major online platform (Blendle). We rely on a multi-equation model to quantify the effect of firm- and consumer-induced communication activities (i.e., newsletter promotions sent out by the platform and consumer likes from readers) on sales and returns and calculate their profitability impact. Our results show that newsletters decrease returns but do not significantly affect sales. In contrast, consumer likes have a twofold effect by increasing sales and decreasing returns. A simulation shows that both newsletters and likes increase profitability and that likes have a higher potential. Our results offer much needed guidance for online aggregators of digital products (e.g., audiobooks, e-books or news articles), as well as for online publishers based on pay-per-unit business models.

The accuracy of alternative supervisory methodologies for the stress testing of credit risk

Following the financial crisis approaches such as regulatory capital ratios have been supplanted by stress testing as a primary tool for managing systemic risks. Financial institutions are mandated to perform stress testing to forecast performance over stress scenarios. While in parallel supervisors conduct their tress tests to set minimum regulatory capital, nothing is revealed regarding the accuracy of such models. We investigate a modelling framework rather close to that of the regulators, projecting financial statement line items for an aggregated 'average' bank. We assess the accuracy of alternative stress testing models, simple single equation versus complex multiple equation approaches. Results show inaccuracies in stress test model forecasts for multi-equation models that do not account for the dependency structure amongst the model variables. This highlights the public policy need for reconsidering the existent regulatory stress tests, and the need for supervisory models to be subject to model validation standards.