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The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

We conduct a detailed simulation study of the forecasting performance of diffusion index-based methods in short samples with structural change. We consider several data generation processes, to mimic different types of structural change, and compare the relative forecasting performance of factor models and more traditional time series methods. We find that changes in the loading structure of the factors into the variables of interest are extremely important in determining the performance of factor models. We complement the analysis with an empirical evaluation of forecasts for the key macroeconomic variables of the Euro area and Slovenia, for which relatively short samples are officially available and structural changes are likely. The results are coherent with the findings of the simulation exercise and confirm the relatively good performance of factor-based forecasts in short samples with structural change.

We thank the Simon Center for Regional Forecasting at the John Cook School of Business at Saint Louis University – especially Jack Strauss, Director of the Simon Center and Ellen Harshman, Dean of the Cook School – for its generosity and hospitality in hosting a conference during the summer of 2006 where many of the chapters appearing in this volume were presented. The conference provided a forum for discussing many important issues relating to forecasting in the presence of structural breaks and model uncertainty, and participants viewed the conference as helping to significantly improve the quality of the research appearing in the chapters of this volume.³ This volume is part of Elsevier's new series, Frontiers of Economics and Globalization, and we also thank Hamid Beladi for his support as an Editor of the series.

The purpose of this paper is to develop a rich and textured narrative that utilises scholarly evidence, empirical research, and practitioner knowledge to shape, inform, and extend understanding of the leadership practice of “excelling at work” as it is enacted for zeitgeist organisational challenges.

In order to achieve this, it traverses a temporal timeline from circa 350 BC to the present millennium, to examine extant theories and concepts and emerging wisdom at the intersection of domains as seemingly diverse as neuroscience, cognitive and social psychology, contemplative practice, positive psychology, and organisational behaviour and leadership.

Complex environments require individual and collective agency for efficacious and adaptive responses. Extant theories and new insights on effectance, meaningful work, signature strengths, purposeful attention, self-control, deliberate practice, grit, explanatory styles, and mindsets amongst others, interconnect and at times intersect to form an empirically validated narrative on the augmented leadership practice of excelling at work in challenging times.

Overcoming zeitgeist challenges adaptively, requires organisations and their people to excel at work. Innovative combinations and connections of key constructs and concepts, underpinned by empirical evidence from a variety of disciplines, explicate the nature and enactments of this vital leadership practice of excelling at work.