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Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Organizational researchers studying well-being – as well as organizations themselves – often place much of the burden on employees to manage and preserve their own well-being. Missing from this discussion is how – from a human resources management (HRM) perspective – organizations and managers can directly and positively shape the well-being of their employees. The authors use this review to paint a picture of what organizations could be like if they valued people holistically and embraced the full experience of employees’ lives to promote well-being at work. In so doing, the authors tackle five challenges that managers may have to help their employees navigate, but to date have received more limited empirical and theoretical attention from an HRM perspective: (1) recovery at work; (2) women’s health; (3) concealable stigmas; (4) caregiving; and (5) coping with socio-environmental jolts. In each section, the authors highlight how past research has treated managerial or organizational support on these topics, and pave the way for where research needs to advance from an HRM perspective. The authors conclude with ideas for tackling these issues methodologically and analytically, highlighting ways to recruit and support more vulnerable samples that are encapsulated within these topics, as well as analytic approaches to study employee experiences more holistically. In sum, this review represents a call for organizations to now – more than ever – build thriving organizations.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Scholars have shown that organizations active in social movements are important because they carry out a number of critical tasks such as recruitment, coordination, and sustained contention. However, these accounts do not explain how a number of recent movements using the tactic of occupation and featuring a seemingly minimal formal organizational structure nevertheless engaged in a number of critical organizational tasks. This paper draws from in-depth ethnographic research on the Occupy Wall Street movement in New York City and finds that the movement’s sustained occupation of Zuccotti Park in Lower Manhattan carried out four critical functions: messaging, recruitment, building commitment, and connecting participants to each other. These findings move past a general overemphasis in the literature on social movements on organizational structure, and instead point toward the utility of a perspective that accounts for the role of nonorganizational factors in the accomplishment of fundamental movement tasks.