In this paper we present a Gibbs sampler for a Poisson model including spatial effects. Frühwirth-Schnatter and Wagner (2004b) show that by data augmentation via the introduction of two sequences of latent variables a Poisson regression model can be transformed into a normal linear model. We...

In this paper we present and evaluate a Gibbs sampler for a Poisson regression model including spatial effects. The approach is based on Frühwirth-Schnatter and Wagner (2004b) who show that by data augmentation using the introduction of two sequences of latent variables a Poisson regression...

We present two alternative approaches for estimating VaR. Both approaches are based on the observation that each trading day is very diverse and we can observe K different phases of the trading day. We can not observe from which of the K phases our observations rt are. Therefore, we apply Gibbs...

This paper provides a structural empirical analysis of Dutch auctions of houseplants at the flower auction in Aalsmeer, the Netherlands. The data set is unique for Dutch auctions in the sense that it includes observations of all losing bids in an interval adjacent to the winning bid. The size of...

In the context of functional estimation, the bootstrap approach amounts to substitution of the empirical distribution function for the unknown underlying distribution in the definition of the functional. A smoothed bootstrap alternative substitutes instead a smoothed version of the empirical...

Der Beitrag schildert die Effekte von Antwortausfällen bei einzelnen Fragen ("item non-response") auf die Ergebnisse von multivariaten statistischen Analysen. Dabei wird das Verfahren der "Data Augmentation" angewendet, um die fehlenden Daten zu ersetzen (Schafer 1997). Anhand von Schätzungen...

This paper provides a structural empirical analysis of Dutch auctions of houseplants at the flower auction in Aalsmeer, the Netherlands. The data set is unique for Dutch auctions in the sense that it includes observations of all losing bids in an interval adjacent to the winning bid. The size of...

In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. We demonstrate that the resulting conditional likelihood of the regression coefficients is multivariate normal, equivalent to a standard Bayesian linear regression, which allows for...

W e develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for...