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1 – 10 of 777Pedro Brinca, Nikolay Iskrev and Francesca Loria
Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of…
Abstract
Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.
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Sadia Samar Ali, Rajbir Kaur and Jose Antonio Marmolejo Saucedo
Qingyuan Wu, Changchen Zhan, Fu Lee Wang, Siyang Wang and Zeping Tang
The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a…
Abstract
Purpose
The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.
Design/methodology/approach
In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.
Findings
Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.
Originality/value
The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.
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This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance…
Abstract
Purpose
This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance characteristics enhance the relationship between sustainability compensation and firms’ non-financial performance and to expand the domain of the impact of sustainability on non-financial performance.
Design/methodology/approach
This analysis is based on a sample of companies listed on the Milan Italian Stock Exchange from the Financial Times Milan Stock Exchange Index over the 2016–2020 period. Regression analysis was used by using data retrieved from the Refinitiv Eikon database and the sample firms’ remuneration reports.
Findings
The findings of this paper show that embedding sustainability in executive compensation positively affects firms’ non-financial performance. The results of this paper also reveal that specific corporate governance features can improve the impact of sustainability on non-financial performance.
Research limitations/implications
This analysis is limited to Italian firms included in the Financial Times Milan Stock Exchange Index; however, the findings are highly significant.
Practical implications
The findings provide regulators with useful insights for considering the integration of sustainability goals into executive remuneration. Another implication is that policymakers should require – at least – listed firms to fulfil specific corporate governance structural requirements. Finally, the findings can provide investors and financial analysts with a greater awareness of the role played by executive remuneration in the long-term value-creation process.
Originality/value
This paper contributes to addressing the relationship among sustainability, remuneration and non-financial disclosure, drawing on the stakeholder–agency theoretical framework and focusing on Italian firms. This issue has received limited attention with controversial results in the literature.
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