Re-employment hazard of displaced German workers: evidence from the GSOEP

This study investigates the re-employment hazard of displaced German workers using the first fourteen sweeps of the German Socio-Economic Panel (GSOEP) data. As well as parametric and non-parametric discrete-time specifications for the baseline hazard, the study employs alternative mixing distributions to account for unobserved heterogeneity. Findings of the study suggest negative duration dependence, even after accounting for unobserved heterogeneity. In terms of covariate effects, those at the lower end of the skills ladder, those who had been working in manufacturing and those with previous experience of non-employment are found to have lower hazard of exit via reemployment.

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.