"It's all up here": adaptation and improvisation within the modern project

This paper considers organisational improvisation, and in particular, adaptation as a specific component of improvisational work(Miner et al., 2001), and how it may assist in resolving or assisting with some of the challenges surrounding recent shifts in our understanding of project-based management. Examples focus on the use of adaptation to cope with ambiguity and uncertainty, caused by execution in problematic and turbulent organisational environments. The literature on improvisation suggests that adapting previously successful interventions reduces and manages the risk of improvising by engaging with the 'adaptation component of organisational improvisation. This practice assists in ensuring that the additional risk of completely novel activity is avoided. This paper explores adaptation within the project domain, and also unpicks the rhetoric from the reality of adaptation within projects, confirming its benefits, setting out the circumstances where experience informs the practice, and offering readily usable and applicable insights

Emotionally sustainable change: two frameworks to assist with transition

Earlier research (Leybourne, 2007; Robinson and Griffiths, 2005; Stensaker et al., 2002) has identified that assisting employees to cope with change can be beneficial in ensuring that change initiatives are more effective. This paper considers two frameworks from outside the 'traditional' change literature that can assist in coping with change and that have been recognised for many years, but which are arguably under-utilised in assisting employees through the behavioural, and particularly the emotional journey through organisational change. Bridges' (1991) transition framework and the Kubler-Ross (1969) Grief Cycle are examined in turn, and each is critically appraised to identify the benefits, or otherwise, of what they offer to assist employees to cope with change, and managers to manage that coping element of change management. The outcomes suggest that both frameworks are beneficial to change practitioners, and can assist in supporting employees through the transition from one organisational state to another

Project knowledge into project practice: generational issues in the knowledge management process

This paper considers Learning and Knowledge Transfer within the project domain. Knowledge can be a tenuous and elusive concept, and is challenging to transfer within organizations and projects. This challenge is compounded when we consider generational differences in the project and the workplace. This paper looks at learning, and the transfer of that generated knowledge. A number of tools and frameworks have been considered, together with accumulated extant literature. These issues have been deliberated through the lens of different generational types, focusing on the issues and differences in knowledge engagement and absorption between Baby Boomers, Generation X, and Generation Y/Millennials. Generation Z/Centennials have also been included where appropriate. This is a significant issue in modern project and organizational structures. Some recommendations are offered to assist in effective knowledge transfer across generational types.Accepted manuscrip

Testing for Stochastic Cointegration and Evidence for Present Value Models

Using the stochastic integration/cointegration framework of Harris, McCabe and Leybourne (2002) we revisit the problem of assessing the empirical evidence for or against the present value class of models in the bond and stock markets. This framework allows for volatility in excess of that catered for by the conventional integration/cointegration paradigm by introducing nonlinear heteroscedasticity. We propose a test for stochastic cointegration against the alternative of no cointegration and a secondary test for stationary cointegration against the heteroscedastic alternative. Asymptotic distributions of these tests under their respective null hypotheses are derived and consistency under their respective alternatives is established. In contrast to conventional cointegration tests, which we show via simulation are unreliable in the presence of the kind of volatility typical of financial data, our tests are able to uncover new cointegration evidence in favour of the present value model, particularly in the bond market.

A powerful test for linearity when the order of integration is unknown [Revised to become No. 07/06 above]

In this paper we propose a test of the null hypothesis of time series linearity against a nonlinear alternative, when uncertainty exists as to whether or not the series contains a unit root. We provide a test statistic that has the same limiting null critical values regardless of whether the series under consideration is generated from a linear I(0) or linear I(1) process, and is consistent against nonlinearity of either form, being asymptotically equivalent to the efficient test in each case. Finite sample simulations show that the new procedure has good size control and offers substantial power gains over the recently proposed robust linearity test of Harvey and Leybourne (2007).Nonlinearity testing; Wald tests; unit root tests; stationarity tests

Robust methods for detecting multiple level breaks in autocorrelated time series

In this paper we propose tests for the null hypothesis that a time series process displays a constant level against the alternative that it displays (possibly) multiple changes in level. Our proposed tests are based on functions of appropriately standardized sequences of the differences between sub-sample mean estimates from the series under investigation. The tests we propose differ notably from extant tests for level breaks in the literature in that they are designed to be robust as to whether the process admits an autoregressive unit root (the data are I(1)) or stable autoregressive roots (the data are I(0)). We derive the asymptotic null distributions of our proposed tests, along with representations for their asymptotic local power functions against Pitman drift alternatives under both I(0) and I(1) environments. Associated estimators of the level break fractions are also discussed. We initially outline our procedure through the case of non-trending series, but our analysis is subsequently extended to allow for series which display an underlying linear trend, in addition to possible level breaks. Monte Carlo simulation results are presented which suggest that the proposed tests perform well in small samples, showing good size control under the null, regardless of the order of integration of the data, and displaying very decent power when level breaks occur.Level breaks; unit root; moving means; long run variance estimation; robust tests; breakpoint estimation

The impact of the initial condition on robust tests for a linear trend

This paper examines the behaviour of some recently proposed robust (to the order of integration of the data) tests for the presence of a deterministic linear trend in a univariate times series in situations where the magnitude of the initial condition of the series is non-negligible. We demonstrate that the asymptotic size and/or local power properties of these tests are extremely sensitive to the initial condition. Straightforward modifications to the trend tests are suggested, based around the use of trimmed data, which are demonstrated to greatly reduce this sensitivity.Trend tests; initial condition; asymptotic local power

Break date estimation for models with deterministic structural change

In this article, we consider estimating the timing of a break in level and/or trend when the order of integration and autocorrelation properties of the data are unknown. For stationary innovations, break point estimation is commonly performed by minimizing the sum of squared residuals across all candidate break points, using a regression of the levels of the series on the assumed deterministic components. For unit root processes, the obvious modification is to use a first differenced version of the regression, while a further alternative in a stationary autoregressive setting is to consider a GLS-type quasi-differenced regression. Given uncertainty over which of these approaches to adopt in practice, we develop a hybrid break fraction estimator that selects from the levels-based estimator, the first-difference-based estimator, and a range of quasi-difference-based estimators, according to which achieves the global minimum sum of squared residuals. We establish the asymptotic properties of the estimators considered, and compare their performance in practically relevant sample sizes using simulation. We find that the new hybrid estimator has desirable asymptotic properties and performs very well in finite samples, providing a reliable approach to break date estimation without requiring decisions to be made regarding the autocorrelation properties of the data

Testing for a unit root when uncertain about the trend [Revised to become 07/03 above]

In this paper we consider the issue of testing for a unit root when it is uncertain as to whether or not a linear deterministic trend is present in the data. The Dickey-Fuller-type tests of Elliott, Rothenberg and Stock (1996), based on (local) GLS detrended (demeaned) data, are near asymptotically efficient when a deterministic trend is (is not) present in the data generating process. We consider a variety of strategies which aim to select the demeaned variant when a trend is not present and the detrended variant otherwise. Asymptotic and finite sample evidence demonstrates that some sophisticated strategies which involve auxiliary methods of trend detection are generally outperformed by a simple decision rule of rejecting the unit root null whenever either the GLS demeaned or GLS detrended Dickey-Fuller-type tests reject. We show that this simple strategy is asymptotically identical to a sequential testing strategy proposed by Ayat and Burridge (2000). Moreover, our results make it clear that any other unit root testing strategy, however elaborate, can at best only offer a rather modest improvement over the simple one.Unit root test; trend uncertainty; initial condition; asymtotic power; union of rejections decision rule

Simple, Robust and Powerful Tests of the Breaking Trend Hypothesis*

In this paper we develop a simple procedure which delivers tests for the presence of a broken trend in a univariate time series which do not require knowledge of the form of serial correlation in the data and are robust as to whether the shocks are generated by an I(0) or an I(1) process. Two trend break models are considered: the first holds the level fixed while allowing the trend to break, while the latter allows for a simultaneous break in level and trend. For the known break date case our proposed tests are formed as a weighted average of the optimal tests appropriate for I(0) and I(1) shocks. The weighted statistics are shown to have standard normal limiting null distributions and to attain the Gaussian asymptotic local power envelope, in each case regardless of whether the shocks are I(0) or I(1). In the unknown break date case we adopt the method of Andrews (1993) and take a weighted average of the statistics formed as the supremum over all possible break dates, subject to a trimming parameter, in both the I(0) and I(1) environments. Monte Carlo evidence suggests that our tests are in most cases more powerful, often substantially so, than the robust broken trend tests of Sayginsoy and Vogelsang (2004). An empirical application highlights the practical usefulness of our proposed tests.Broken trend, power envelope, unit root, stationarity tests