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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.

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Women from many cultures have historically been closely tied to the land and the environment through their role as subsistence farmers. But as the more developed nations have shifted to commercial agriculture and improved technology, farming has become a male-dominated industry. China’s shift from traditional family-operated farms to government-controlled collectives required a system of incentives to encourage agricultural labor to remain and prevent mass exodus to the cities. Hukou was created in the 1950s as a system of governmental registration for restricting the internal migration of labor within China, identifying citizens’ residency by place of birth. Residents of rural or urban locations are classified agricultural or nonagricultural labor, respectively. But as China’s industrialization has grown and technology has reduced the need for human agricultural labor, the need and desire for urban employment has intensified. For women, relocating has changed marriage practices, influenced child rearing, and altered their right to land tenure in their home region. This paper examines the role of gender in the changing use of hukou in the development of China, focusing on the impact of women’s patterns of migration on land tenure. Although hukou policies are still changing and there is a lack of data on the most recent changes, initial studies show that there are few who wish to give up their rural hukou in order to obtain urban hukou. Changes over the past decade indicate that rural woman are not only taking on more of the agricultural workload as men are drawn to urban employment, but also that they are less likely to care about environmental degradation in China.

Using Kunming, the capital of China's southwest Yunnan Province, as an example, this mixed-methods research examines three interacting dimensions of social change in contemporary China: migration, ethnicity, and education. In particular, it sheds light on the issue of educational achievement of migrant children, especially children of ethnic minority background. The quantitative portion of the study is based on data gathered from over 700 sample students, teachers, and principals who participated in the “2008 Kunming Migrant Children's Survey.” A two-level hierarchical linear modeling (HLM) is employed to identify student- and school-level factors and to estimate the impacts of these factors on migrant children's academic achievement. The qualitative portion of the study is based primarily on the data collected through in-depth individual interviews and focus-group discussions with 97 migrant students, teachers, and school principals from 10 government and nongovernment migrant children's schools in Kunming between 2008 and 2009. The qualitative and quantitative results highlight four interrelated groups of educational barriers experienced by migrant students in pursuing compulsory education: institutional, socioeconomic, cultural, and psychological barriers. In particular, cultural and psychological barriers, including difficulty in school adaptation, low self-esteem, lack of family support, and discrimination against ethnic minorities due to their different religious beliefs and ethnic traditions, are found to have exerted particularly significant negative influences on academic achievements of ethnic minority students.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

Improved production with quality, safety, and security is the biggest challenge of the food industry. Modern technologies, including robotics and automation systems, can help to cope with such issues. This chapter gives a brief view of robotics and automation for the sustainable food industry along with packaging, warehousing, distribution, marketing, and consumer services. It describes the recently implemented solutions of robotic automation in different supply chain operations and various food commodities. The benefits of robotic and automation technology for perishable and semi-perishable items have also been covered. The present research may assist the food industry professionals, supply chain managers, and academicians in implementing automation and robotics in the food industry.

This chapter aims to analyze how digital entrepreneurship is developed in the food industry of the European Union, comparing digital skills and big data indicators in all enterprises and the food industry. Using Eurostat Digital Economy and Society database, the authors obtained data between 2016 and 2020 – including the indicators: information and communications technology (ICT) specialists and ICT training to digital skills, and smart devices, geolocation, and social media to big data assessment. Furthermore, we compared all enterprises with those that manufacture beverages, food, and tobacco products. The authors identified that the food sector is still behind the other sectors regarding digitalization. Consequently, this research contributes to understanding entrepreneurs’ digital skills and how them relate to the use of big data in the food industry. Moreover, it also allows identifying the digital indicators of the food industry as less innovative than other industry digital indicators.