What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.Bayesian, model selection, model averaging, count data, zero-inflation, demand for health care

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

This paper uses an infinite hidden Markov model (IHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and attractive alternative to existing methods. We found a clear structural break during the recent financial crisis. Prior to that, inflation persistence was high and fairly constant.inflation dynamics, hierarchical Dirichlet process, IHMM, structural breaks, Bayesian nonparametrics

Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach

This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specifed in a semiparametric Bayesian fashion using a Dirichlet process prior. This results in a very exible mixture distribution with an in nite number of components for the random effects. Therefore, the model can be seen as a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo (MCMC)simulation methods is discussed. The methodology is illustrated with an application using data from Germany

Children’s information retrieval: beyond examining search strategies and interfaces

The study of children’s information retrieval is still for the greater part untouched territory. Meanwhile, children can become lost in the digital information world, because they are confronted with search interfaces, both designed by and for adults. Most current research on children’s information retrieval focuses on examining children’s search performance on existing search interfaces to determine what kind of interfaces are suitable for children’s search behaviour. However, to discover the true nature of children’s search behaviour, we state that research has to go beyond examining search strategies used with existing search interfaces by examining children’s cognitive processes during information-seeking. A paradigm of children’s information retrieval should provide an overview of all the components beyond search interfaces and search strategies that are part of children’s information retrieval process. Better understanding of the nature of children’s search behaviour can help adults design interfaces and information retrieval systems that both support children’s natural search strategies and help them find their way in the digital information world

Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks

This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structual breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We find that, in-sample, these extensions clearly are warranted. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than the inclusion of breaks. Classification-JEL:

The semistatic limit for Maxwell's equations in an exterior domain

This paper provides a $L^p$-theory for the Maxwell-system in the semistatic limit case in an exterior domain. The problem under consideration is of mixed type, since the possibly nonlinear electric conductivity vanishes on a certain subset of the domain. The solution is obtained from a singular perturbation of the full Maxwell-system including the displacement current

Modeling the Dynamics of Inflation Compensation

This paper investigates the relationship between short-term and long-term ination expectations using daily data on ination compen- sation. We use a exible econometric model which allows us to uncover this relationship in a data-based manner. We relate our …ndings to the issue of whether ination expectations are anchored, unmoored or contained. Our empirical results indicate no support for either unmoored or …rmly anchored ination expectations. Most evidence indicates that ination expectations are contained.