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Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Technological change drives transformation in most sectors of the economy. Industry 4.0 technologies have been applied at different stages of a building’s lifecycle. However, limited studies exist on their application in real estate facilities management (REFM). This study aims to assess the existing knowledge on the topic to suggest further research directions.

Scopus-indexed literature from 2013 to 2023 was examined and visualised using VOSviewer software to output quantitative (descriptive) results. Content analysis was used to complement the quantitative findings.

Findings indicated a concentration of research in China, Norway and Italy. The knowledge areas included three clusters: lifecycle integration and management, data curation and management and organisational and management capabilities. The benefits, challenges and support strategies were highlighted.

More collaboration is needed across countries and territories on technology integration in REFM. Future research using alternative methodologies is recommended, with a focus on adopting and non-adopting REFM organisations. Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

Further, implications for facility managers, employees, technology suppliers or vendors, training, organisations and management exist.

The study reveals the knowledge base on technology use in REFM. It adds to the evidence base on innovation and technology adoption in REFM.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

School violence can cause or exacerbate individual and collective trauma. Trauma-informed school approaches offer schools and educators guidance for how to respond. In this chapter, we provide an overview of trauma-informed school approaches and their contributions to healing individual and collective trauma. We begin this chapter by addressing the complex intersection of disability and trauma, and the unique implications of school-based violence for students with disabilities and their teachers. We then define trauma-informed care, describe current short- and long-term trauma-informed school approaches, and explain the aims of these approaches at individual and collective levels. Next, we locate trauma-informed responses to school violence in a context of systemic trauma and share considerations for disrupting the systemic conditions that perpetuate trauma and school violence. We discuss critiques of the trauma-informed care movement and conclude with recommendations for scholars pursuing research in this area.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Exploring subjective experiences of people living with dementia through qualitative research has become increasingly common in recent decades. Nonetheless, researchers have shared a number of ethical challenges in involving people living with dementia in research. A concept that has been influential in discussions about ethics within the field of dementia care, in particular, is person-centredness. A person-centred approach reflects values of respect for personhood and the rights of a person and of building mutual trust and understanding. This chapter presents my experience of adopting person-centred ethical practices in a sensory ethnographic study involving older adults living with dementia. I highlight person-centred ethical considerations at the design stage of my study and occasions during the conduct of my research when research methods and processes were adapted to further meet the needs of the participants. A person-centred approach required that I continually assessed the need to make ethical decisions in every aspect of the research process throughout its duration. Building and drawing on positive researcher–participant relationships to inform those decisions and an adaptable research design allowing research practices to be adapted in situ were therefore essential.

Individuals who are exploited and manipulated by criminal gangs are often wrongly labelled, stereotyped, and blamed (to varying levels) for the vulnerable positions that they find themselves in. Individuals who perpetrate violence towards others have often also been victims of violence and crime themselves. Teaching about these groups and trying to represent their position and experiences is a difficult and sensitive area. Building on current research, experiences of teaching professionals and students about the exploitation of marginalised groups, serious violence, and crime during COVID-19, are reflected upon. In doing so, what is important for students to understand about marganilised groups is set out, and recommendations for improvements to teaching practice are discussed. Firstly, it is argued that where educators seek to help students learn about marginalised groups, there needs to be preparation to put the work in at the front end and reflect on their own assumptions, beliefs and learning needs. Secondly, they also need to think about the ‘active’ and ‘passive’ (Seddon, 2005) aspects of individual agency and to be as authentic as they can to the lived experiences of marginalised communities as community members engage in types of behaviours and access services. Finally, it is then important for them to open up opportunities for students to reflect on the more detailed aspects and wider social, economic, and structural factors that marginalised communities face as they endeavour to articulate their own needs in society.

The purpose of this study is to understand user perceptions and misconceptions regarding security tools. Security and privacy-preserving tools (for brevity, the authors term them as “security tools” in this paper, unless otherwise specified) are designed to protect the security and privacy of people in the digital environment. However, inappropriate use of these tools can lead to unexpected consequences that are preventable. Hence, it is significant to examine why users do not understand the security tools.

The authors conducted a qualitative study with 40 participants in the USA to investigate the prevalent misconceptions of people regarding security tools, their perceptions of data access and the corresponding impact on their usage behavior and data protection strategies.

While security vulnerabilities are often rooted in people’s internet usage behavior, this study examined user’s mental models of the internet and unpacked how the misconceptions about security tools relate to those mental models.

Based on the findings, this study offers recommendations highlighting the design aspects of security tools that need careful attention from researchers and industry practitioners, to alleviate users’ misconceptions and provide them with accurate conceptual models toward the desired use of security tools.