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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.

Nuclear energy investments contribute significantly to the energy independence of countries. This situation helps countries to develop both economically and socially. Therefore, countries need to develop strategies to increase their nuclear energy investments. However, radioactive waste generated in nuclear reactors is one of the most important disadvantages of this type of energy. Due to this situation, the wastes generated must be disposed of effectively for nuclear energy investments to be effective. In this study, it is aimed to create optimal strategies for the effective waste managements generated in nuclear power plants. Within this framework, four different balanced scorecard perspectives are considered as the criteria. In the analysis process, Analytical Hierarchy Process (AHP) methodology is taken into consideration. It is identified that learning and development is the most critical factor for the effective radioactive waste management created in nuclear power plants. On the other side, internal factors are the second most important criterion in this framework. Hence, it is understood that nuclear energy investors should give priority to the technological development for effective waste management in nuclear power plants. With the help of these technological improvements, the wastes can be disposed more successfully. As a result, environmental sustainability can be provided much easily.

This chapter examines how information and communication technology (ICT) initiatives affect customers’ everyday routines with an emphasis on how electronic word of mouth (eWOM) affects their purchasing decisions and determines if eWOM might serve as a useful trust factor when making purchasing decisions. This chapter is based on both primary and secondary data and examines how eWOM affects customers’ buying decisions in this era of increasing social media platform usage. One hundred individuals from various regions of Karachi provided the primary data, collected through an online survey. Secondary data are also employed, such as details from business websites, scientific papers, and other related publications of businesses. In Karachi’s developing digital market, it was discovered that eWOM via social media used to have a substantial impact on consumers’ shopping habits. The effectiveness of social media content depends greatly on how appropriate it is for the intended audience. Social media marketing doesn’t quite simply aim to improve consumers’ impressions or directly advertise things. It also entails maintaining and fostering relationships between businesses and prospective customers. According to the research, consumers’ recommendations and ratings of goods and services made on social media, whether through eWOM or another channel, influenced their decisions to buy. Customers consider social media to be trustworthy when making decisions about what to buy. Customers are currently adopting social media as opposed to more conventional means to learn about new products. Most customers believe that eWOM from social media greatly affected their shopping choices, according to the results of the study.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

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.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

Purpose: Students’ transition from education to employment is influenced by factors like the length and calibre of their education, demography, labour market conditions, and the general state of the economy. Regardless of the economy, education systems should seek to ensure that students have the skills required for the labour market. This will help them better transition from school to work. This study examines the work skills that companies require for entry-level positions in Romania.

Need for Study: Previously, text analysis studies treated the job market only for the IT industry in Romania. To understand the demand-side opportunities and restrictions, assessing the employment opportunities for young people in the Romanian labour market is necessary.

Methodology: A text mining approach from 842 unstructured data of the existing job positions in October 2022 for fresh graduates or students is used in this chapter. The study uses data from LinkedIn job descriptions in the Romanian job market. The methodology involved is focused on text retrieval, text-pre-processing, word cloud analysis, network analysis, and topic modelling.

Findings: The empirical findings revealed that the most common words in job descriptions are experience, team, work, skills, development, knowledge, support, data, business, and software. The correlation network revealed that the most correlated pairs of words are gender–sexual–race–religion–origin–diversity–age–identity–orientation–colour–equal–marital.

Practical Implications: This study looked at the job market and used text analytics to extract a space of skill and qualification dimensions from job announcements relevant to the Romanian employment market instead of depending on subjective knowledge.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.