The Impact of Accident Attention,Ideology, and Environmentalism on American Attitudes Toward Nuclear Energy

This study involves the analysis of three waves of survey data about nuclear energy using a probability‐based online panel of respondents in the United States. Survey waves included an initial baseline survey conducted in early 2010, a follow‐up survey conducted in 2010 following the Deepwater Horizon oil spill in the Gulf of Mexico, and an additional follow‐up conducted just after the 2011 Fukushima, Japan, nuclear accident. The central goal is to assess the degree to which changes in public views following an accident are contingent on individual attention and respondent predispositions. Such results would provide real‐world evidence of motivated reasoning. The primary analysis focuses on the impact of Fukushima and how the impact of individual attention to energy issues is moderated by both environmental views and political ideology over time. The analysis uses both mean comparisons and multivariate statistics to test key relationships. Additional variables common in the study of emerging technologies are included in the analysis, including demographics, risk and benefit perceptions, and views about the fairness of decisionmakers in both government and the private sector.     

Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads

This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.     

Factors affecting social presence and word-of-mouth in corporate social responsibility communication: Tone of voice,message framing,and online medium type

To understand how tone of voice, message framing, and type of online media affect public perceptions and reactions to an organization in the context of corporate social responsibility (CSR) communication, this study conducted a 2 (tone of voice: human voice vs. organizational voice) x 2 (message framing: gain-focused vs. loss-focused) x 2 (online media: Facebook vs. organizational blog) online experiment (N?=?394). Conversational human voice and gain-focused framing significantly influence the social presence of the organization and publics’ positive word-of-mouth intention. Publics’ intention to generate positive word-of-mouth was highest when the organization used conversational human voice with gain-focused message and conveyed the message on its Facebook page.     

Bayesian meta-analysis of correlation coefficients through power prior

This article proposes a Bayesian approach for meta-analysis of correlation coefficients through power prior. The primary purpose of this method is to allow meta-analytic researchers to evaluate the contribution and influence of each individual study to the estimated overall effect size though power prior. We use the relationship between high-performance work systems and financial performance as an example to illustrate how to apply this method. We also introduce free online software that can be used to conduct Bayesian meta-analysis proposed in this study. Implications and future directions are also discussed in this article.     

Analysis of strengths and weaknesses in the National Human Resource Development systems of BRICS

National Human Resource Development (NHRD) takes a humane and long-term perspective to the human resources of each country in terms of national growth. This research evaluated the human resource development competitiveness of Brazil, Russia, India, China, South Africa (BRICS) countries from the perspective of NHRD. This study selected Global Human Resource Competitiveness Index to assess the NHRD of BRICS. Although it is generally considered that the BRICS countries have high growth potential with large population, this analysis showed that they have a common weakness in the balanced development and use of human resources from the perspective of NHRD. In this respect, this study compares and analyses the characteristics and strength/weakness of each BRICS country. This research can inform evaluation of policies on national human resource development. Additionally, it can provide basic data for identifying the status of the human resource development system.     

Regional Integration,Multinational Enterprise Strategy and the Impact of Country‐level Risk: The Case of the EMU

The European Monetary Union (EMU) provides a new macro‐level, institutional setting for multinational enterprises (MNEs). The authors investigate the impact of regional integration on MNE strategy by analysing Belgian firms’ entry‐mode choices in foreign markets, both EMU and non‐EMU ones, with a focus on what impact remains of country‐level risk. They demonstrate that regional integration has altered the impact of country‐level institutional risk on MNE entry‐mode choices inside the EMU. The conventional predictions of international business theory have been reversed, with higher country‐level risk inside the EMU driving a preference for wholly owned subsidiaries. Within the integrated region, insider firms now view higher country‐level risk as the equivalent of higher, micro‐level contracting risk. Such risk can best be mitigated through full internalization, combined with arm's length contracts, rather than through equity joint ventures.     

Robust precision matrix estimation via weighted median regression with regularization

A precision matrix is an important parameter of interests because its elements describe useful association information among multiple variables, which has a wide variety of applications. For example, it is used for inferring gene regulation networks in genomic studies and stock association networks in financial studies. However, in many cases, the precision matrix needs to be robustly estimated due to the presence of outliers. We propose estimating a sparse scaled precision matrix via weighted median regression with regularization. Our weighted median regression approach is consistent under various distributional assumptions including multivariate t‐ or contaminated Gaussian distributions. This fact is illustrated with simulation studies and a real data analysis with monthly stock return data. The Canadian Journal of Statistics 46: 265–278; 2018 © 2018 Statistical Society of Canada     

Diagnostics for repeated measurements in generalized linear mixed effects models

ABSTRACT

As there is an extensive body of research on diagnostics in regression models, various outlier detection methods have been developed. These methods have been extended to mixed effects models and generalized linear models, but there exist intrinsic drawbacks and limitations. This paper presents two-dimensional plots to identify discordant subjects and observations in generalized linear mixed effects models, displaying discordance in two directions. The sTudentized Residual Sum of Squares is not an extension of any regression tools but a new approach designed to efficiently reflect the characteristics of repeated measures. And this noteworthy clustering of outliers is identified in the plot. Applications to real-life examples are presented to illustrate the favorable/beneficial performance of the new tool.     

Bayesian tests for unit root and multiple breaks

A Bayesian approach is considered for identifying sources of nonstationarity for models with a unit root and breaks. Different types of multiple breaks are allowed through crash models, changing growth models, and mixed models. All possible nonstationary models are represented by combinations of zero or nonzero parameters associated with time trends, dummy for breaks, or previous levels, for which Bayesian posterior probabilities are computed. Multiple tests based on Markov chain Monte Carlo procedures are implemented. The proposed method is applied to a real data set, the Korean GDP data set, showing a strong evidence for two breaks rather than the usual unit root or one break.