The paper investigates maternity leave behavior in West Germany for females being employed between 1995 and 2006 using data from the German Socio Economic Panel. The observational study focuses on the investigation of individual and family-related covariate effects on the duration of maternity...

The paper investigates maternity leave behavior in West Germany for females being employed between 1995 and 2006 using data from the German Socio Economic Panel. The observational study focuses on the investigation of individual and family-related covariate effects on the duration of maternity...

This paper shows that there are severe measurement errors regarding the occupational affiliations in the German Socio-Economic Panel. These errors are traced back to the survey structure: in years where occupational information is gathered from the entire employed population instead of only from...

This paper shows that there are severe measurement errors regarding the occupational affiliations in the German Socio-Economic Panel. These errors are traced back to the survey structure: in years where occupational information is gathered from the entire employed population instead of only from...

Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function....

This paper aims to investigate a Bayesian sampling approach to parameter estimation in the semiparametric GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This...

We approximate the error density of a nonparametric regression model by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. We investigate the construction of a likelihood and posterior for bandwidth parameters under this...

Kernel density estimation is an important technique for understanding the distributional properties of data. Some investigations have found that the estimation of a global bandwidth can be heavily affected by observations in the tail. We propose to categorize data into low- and high-density...

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibit a high-level of skewness...

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time-varying coefficients in time series models. We establish...