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This paper considers the links between knowledge management, improvisational working and project management, highlighting their confluence in a number of key dimensions and focusing on the way new knowledge is generated, shared, and applied within the project domain. The opportunities for improved management in contexts of ubiquitous novelty, ambiguity, and compressed time, particularly in project‐based domains, are explored with reference to these key dimensions and recommendations for practice and further research suggested. Copyright © 2015 John Wiley & Sons, Ltd.

Augmented Dickey–Fuller regressions on pooled (but not individual) real exchange rates for the post–1973 period consistently reject the unit root null, even after accounting for cross–sectional dependence. The inference that the series is stationary, however, is not necessarily correct, because these tests strongly over–reject the null in certain circumstances, particularly when the series has a stochastic unit root. We find that bilateral real exchange rates against the US dollar have a stochastic unit root. Out–of–sample prediction exercises for an autoregressive model confirm these findings.

In this paper we test whether GDP series in 12 European Union countries are integrated or are stationary around a deterministic component that may change gradually and smoothly between two regimes over time. We find that in two‐thirds of cases there appears to be a role for modelling with deterministic functions that allow smooth transitions, in some cases standing alone, in others in conjunction with additional integrated regressors. These findings constitute a challenge to traditional approaches to modelling breaking‐trend behaviour in GDP, which typically impose the condition that breaks, when present, must occur instantaneously.

In this paper we suggest a new and consistent procedure designed to test for the presence of unit roots in quarterly time series, be they at the zero or seasonal frequencies. For each frequency the proposed statistic is based on the correlogram of a transformation (which removes unit roots that may exist at frequencies other than that of interest) of the residuals from a regression of the observed series on relevant deterministic components. Each statistic is proportional to the maximum lag length for which the residual autocorrelations at all lower lag lengths are strictly positive. Critical values for each test statistic are calculated using Monte Carlo simulation assuming a seasonal random walk data‐generation process (DGP). The robustness properties of each statistic to different integrated DGPs are examined along with their respective power characteristics. These simulations indicate that the procedure suggested in this paper provides a useful complement to the set of test statistics proposed by Hylleberg et al. ('Seasonal integration and cointegration', Journal of Econometrics, Vol. 44 (1990), pp. 215–238).