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Considers the daily production‐scheduling problem in the “make‐to‐order” apparel‐manufacturing industry and presents a solution procedure for the problem based on the simulated annealing technique. The development is aimed at the quick generation of a feasible solution and the improvement on the solution.

The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA.

Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing.

The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works.

The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

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.

This is a survey paper of the recent literature on the application of semiparametric–econometric advances to testing for functional form of the environmental Kuznets curve (EKC). The EKC postulates that there is an inverted U-shaped relationship between economic growth (typically measured by income) and pollution; that is, as economic growth expands, pollution increases up to a maximum and then starts declining after a threshold level of income. This hypothesized relationship is simple to visualize but has eluded many empirical investigations. A typical application of the EKC uses panel data models, which allows for heterogeneity, serial correlation, heteroskedasticity, data pooling, and smooth coefficients. This vast literature is reviewed in the context of semiparametric model specification tests. Additionally, recent developments in semiparametric econometrics, such as Bayesian methods, generalized time-varying coefficient models, and nonstationary panels are discussed as fruitful areas of future research. The cited literature is fairly complete and should prove useful to applied researchers at large.

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

Illustrates the need for incorporating time‐phased operator efficiency levels in apparel production plans. Presents a list of factors which predict how long it will take an operator to learn a sewing operation. Describes prediction models which use these factors to estimate time‐phased operator performance under different conditions.

The definition of the term “family” around the Western world is more heterogeneous than ever before and so are its roles and the social expectations of it. However, prisoners’ families (specifically parents and siblings) are expected to support their incarcerated son/brother as they are perceived responsible for his choices and as having the closest relationship with him. Based on a study of parents and siblings of incarcerated men in Israel, this chapter’s goal is to shed light on families’ choice to support their incarcerated son or brother and the struggles this choice entails. A thematic analysis of semi-structured interviews with 17 parents and 10 siblings of incarcerated men in Israel showed that nuclear family members may experience various struggles throughout the legal proceedings, including family hardships, negative social experiences, and negative experiences with formal institutions – all leading to social self-exclusion. Looking through the intersectionality lens, the findings show that when accumulating hardships that prisoners’ families experience encounter perceived harsh institutional systems of oppression, preordained marginalization can be deepened as families operate in opposition.

The chapter provides a brief review of team diversity research from its roots in group composition and workforce demographics through recent trends toward dynamic multilevel models. The divergence from this research area's early motivations in social justice and inclusion to a primarily economic motivation is highlighted. The chapter also reviews major theories that have been used to explain diversity effects in team interactions and outcomes. The review leads to a discussion of three broad critiques of mainstream diversity research, namely a predominance of US cultural outlook, a narrow disciplinary base in organization sciences and psychology, and inadequate attention and overly simplistic perspective on communication processes. The chapter ends with implications for team diversity research and a discussion of how the suggestions can be applied to emerging dimensions of diversity.

The purpose of this paper is to investigate the endogeneity of asset values and how it relates to farm financial stress in US agriculture. The authors conceptualize an implied measure of farm financial stress as a function of debt position. The authors posit that there are variations in the asset values that are beyond the farmer’s control and therefore have implications on farm debt.

The framework recognizes the endogeneity of return on assets (ROA). It uses a non-parametric technique to approximate the variance of expected ROA (VEROA). The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Further, the authors use a dynamic balanced panel data set from 1960 to 2011 for 15 US agricultural states from the Agricultural Resource Management Survey, and information from traditional state-level financial statements.

Estimation of linear dynamic debt panel data models accounting for the endogeneity of ROA and VEROA is a challenging task. Estimated variances are unstable. Hence, the authors focus on variance specification that uses the residuals squared from the ARIMA specification and non-parametric estimators. Arellano-Bover/Blundell-Bond generalized method of moments estimation procedures, although may be biased, show that VEROA has a negative and significant effect on the total amount of debt in the agricultural sector.

The instruments used in this analysis are lagged regressors which may be weakly correlated with the relevant first-order condition, hence not properly identifying the parameters of interest. Future research could include the identification of better instruments, potentially use of sequential moment conditions.

Unlike previous study, the authors use non-parametric approximation of VEROA. The authors model the rate of return on agricultural assets and interest rate with a formulation that focuses on macroeconomic policy. Second, the authors make use of a large dynamic balanced panel data set from 1960 to 2011 for 15 agricultural states in the USA. To the best of the authors’ knowledge, this study is one of the few that provides evidence on risk-balancing behavior at the agricultural sector level, of the USA.

The purpose of this paper is to provide an overview on the state of the field in intercultural dynamics on competition and cooperation at the individual, team, and organizational levels. The authors integrate previous studies from multiple disciplines to articulate the contextual importance of intercultural dynamics. The authors also suggest three overarching themes to expand the field of research on intercultural dynamics.

The authors use an integrative literature review to articulate the importance of intercultural dynamics, provide an introduction to the new contributions in this special issue, and propose new directions for future research.

Intercultural dynamics research has the potential to expand in three overarching areas: constructive controversy, collaborative communication, and global competency and identity at multiple levels.

Intercultural dynamics is still a nascent field emerging from cross-cultural and strategic management. The authors hope the review lays the groundwork for more studies on intercultural dynamics at the interpersonal, team, organizational, and mixed levels of analysis in both theory building and empirical works.

Understanding intercultural dynamics in competition and cooperation can help individuals and managers in multinationals and born global organizations navigate cultural complexity and foster cooperation.

The authors hope the ideas on intercultural dynamics can facilitate collaboration and reduce conflict in intercultural encounters at the individual, organization, and societal levels.

This paper offers an overview on the state of the field and lays groundwork for more systematic inquiries on intercultural dynamics in competition and cooperation.