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This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

Our theoretical understanding of subjective well-being in the workplace is incomplete without a dynamic understanding of antecedents and outcomes of subjective well-being. While between-person differences provide useful information about employee outcomes, these differences do not provide information about the relationships between subjective well-being and employee outcomes that evolve over time and across situations. In this paper, we discuss specific statistical methods within the nomothetic and idiographic perspectives that can support dynamic research on subjective well-being in the workplace and outline unanswered contemporary questions regarding structure, processes, and dynamics of subjective well-being that may be addressed with these methods reviewed; some of which were proposed in early research but progressed slowly due to a lack of adequate methods. This discussion highlights how idiographic methods from outside organizational psychology can be applied to the study of worker subjective well-being to strengthen this dynamic approach in a way that addresses limitations associated with reliance on between-person models.

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

A dynamic oligopoly model of the cigarette industry is developed to study the responses of firms to various antismoking policies and to estimate the implications for the policy efficacy. The structural parameters are estimated using a combination of micro and macro level data and firms’ optimal price and advertising strategies are solved as a Markov Perfect Nash Equilibrium. The simulation results show that tobacco tax increase reduces both the overall smoking rate and the youth smoking rate, while advertising restrictions may increase the youth smoking rate. Firm’s responses strengthen the impact of antismoking policies in the short run.