Many scholarly attempts to ascribe meaning to contemporary employment have adopted terms such as 'new' or 'boundaryless' careers.We argue that it makes more sense to conceptualize careers as 'bounded' than as 'boundaryless'. We argue that careers are bounded by prior career history, occupational identity and by institutional constraints imposed by 'gatekeepers' to job opportunities. We present an empirical study of employment outcomes in a mediated labour market. Drawing on placement history and CV data from IT professionals, we examine the impact of occupation-specific human capital, prior career mobility and agency relationships on the probability of being shortlisted for a vacancy. We find that a candidate's prior history with the recruitment agency is a more important factor than occupation-specific human capital in determining access to job vacancies, indicating that intermediaries structure labour market opportunities. Even in a high-turnover industry, prior career mobility has a negative effect on access to permanent vacancies. K E Y WO R D Sboundaries boundaryless careers gatekeepers human capital information technology intermediaries

No abstract

Reading's research outputs online 1 NOTICE: this is the author's version of a work that was accepted for publication in the International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in the International

In this paper, a set of appropriately modified information criteria for selection of models from the AR-GARCH class is derived. It is argued that unmodified or naively modified traditional information criteria cannot be used for order determination in the context of conditionally heteroscedastic models. The models selected using the modified criteria are then used to forecast both the conditional mean and the conditional variance of two high frequency exchange rate series. The analysis indicates that although the use of such model selection methods does lead to significantly improved forecasting accuracies for the conditional variance in some instances, these improvements are by no means universal. The use of these criteria to jointly select conditional mean and conditional variance model orders leads to performance degradation for the conditional mean forecasts compared to models which do not allow for the heteroscedasticity.Akaike information criterion, Schwarz information criterion, GARCH, high frequency financial data, exchange rate prediction, volatility forecasting,

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1,1) model. An examination of the orders of models selected by the criteria reveals that (1,1) models are typically selected less than 20% of the time.