In this paper we analyze the sources of German unemployment within a structural vector error correction model (SVECM) framework. For this purpose, we propose a method to estimate an exactly identified Subset SVECM, which is a SVECM with short run parameter restrictions. A cointegration analysis...

We provide empirical evidence on the nature of spatial externalities in a matching model for the UK. We use a monthly panel of outflows, unemployment and vacancy stocks data from the registers at Jobcentres in the UK; these are mapped on to travel-to-work areas. We find evidence of significant...

The regional distribution of unemployment rates in the Czech Republic over the transition period is shown to be characterized by twin peaks, e.g. a high and a low unemployment equilibrium. The emergence of strong regional disparities at the beginning of the 1990s can, at least partially, be...

Amid lively debate on the consequences of temporary employment, the paper examines the wages and transitions of temporary employees in Germany using socio-economic panel data from the late 1990s. Compared to simple OLS estimates, using a fixed effects model decreases wage differentials between...

This paper examines the relationship between unemployment, real oil price and real interest rates in Canada. Instead of following the classical approach based on I(0) stationarity or I(1) cointegrating relationships, we use fractional integration/cointegration techniques which allow for the...

This article is concerned with the dynamic behaviour of UK unemployment. However, instead of using traditional approaches based on I(0) stationary or I(1) (integrated and/or cointegrated) models, we use the fractional integration framework. In doing so, we allow for a more careful study of the...

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. On the other hand, semiparametric and nonparametric methods, which are not restricted by parametric assumptions, require more data...

In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...

It is common practice to identify the number and sources of shocks that move implied volatilities across space and time by applying Principal Components Analysis (PCA) to pooled covariance matrices of changes in implied volatilities. This approach, however, is likely to result in a loss of...

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum...