Simon P. Burke

The ‘bounded' career: An empirical study of human capital, career mobility and employment outcomes in a mediated labour market

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 candidates 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.

Benchmarks and the accuracy of GARCH model estimation

This paper reviews nine software packages with particular reference to their GARCH model estimation accuracy when judged against a respected benchmark. We consider the numerical consistency of GARCH and EGARCH estimation and forecasting. Our results have a number of implications for published research and future software development. Finally, we argue that the establishment of benchmarks for other standard non-linear models is long overdue.

Modelling non-stationary economic time series: a multivariate approach

Modelling non-stationary economic time series , Modelling non-stationary economic time series , کتابخانه دیجیتال جندی شاپور اهواز

Forecasting exchange rate volatility using conditional variance models selected by information criteria

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.

The determinants of the location of foreign direct investment in UK regions

This article employs a database of over 2000 observations of Foreign Direct Investment (FDI) projects in UK regions. We analyse this data by means of various multinomial and conditional logit models in order to identify the major determinants of the location choices of these inward investments. Having controlled for the various characteristics of inward investing firms, the projects and the regions, our results suggest that existing regional specialization is the single most important determining feature of where inward FDI locates. In addition, London is seen to benefit primarily by the immigration of new investments, the majority of which are related to service sector activities.

Information criteria for GARCH model selection

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.

Common Trends, Cointegration and CompetitivePrice Behaviour

This article describes a characterisation of competitive market behaviour using the concepts of cointegration analysis. It requires all (n) firms to set prices to follow a single stochastic trend (equivalently the vector of n prices should have cointegrating rank n-1). This implies that, in the long run, prices are driven by the shocks that impact on all companies, ruling out the possibility that the price set by any one firm is weakly exogenous.

Arbitrage, Market Definition and Monitoring a Time Series Approach

This article considers the application to regional price data of time series methods to test stationarity, multivariate cointegration and exogeneity. The discovery of stationary price differentials in a bivariate setting implies that the series are rendered stationary by capturing a common trend and we observe through this mechanism long-run arbitrage. This is indicative of a broader market definition and efficiency. The problem is considered in relation to more than 700 weekly data points on gasoline prices for three regions of the US and similarly calibrated simulated series. The discovery of a single common trend is consistent with competitive pricing and a broad market definition, but the finding of a single weakly exogenous variable affects this conclusion.

Data-dependent selection of the lag truncation parameter in unit root tests of the Phillips-Perron type

Abstract The Andrews (Econometrica, 1991, 59, 817–858) plug-in method of heteroscedastic and autocovariance consistent covariance matrix estimation is used to construct estimators of the long-run variance parameter for use in Phillips-Perron unit root tests. This allows the lag truncation parameter to be data dependent. Monte Carlo size and power estimates are obtained suggesting that this apparently natural approach does not provide significant improvements in test performance.

Some results on the finite sample significance levels of instrumental variable tests for non-nested models

Abstract Monte-Carlo estimates of the finite sample significance levels of a number of instrumental variable tests for non-nested models are provided. A simple generalization of the J-test performs better than other procedures.