This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The...

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...

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...

We develop a new method to sample from posterior distributions in hierarchical models without using Markov chain Monte Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models involving large data sets. Samples are independent, so...

Heckman selection model is the most popular econometric model in analysis of data with sample selection. However, selection models with Normal errors cannot accommodate heavy tails in the error distribution. Recently, Marchenko and Genton proposed a selection-t model to perform frequentist’...

Der Beitrag schildert die Effekte partieller Antwortverweigerung auf die Ergebnisse multivariater statistischer Analysen anhand von Produktivitätsschätzungen auf Betriebsdatenebene. Als Datenbasis dient das IAB-Betriebspanel aus dem Jahr 2000, wobei fehlende Daten mit Hilfe des "Data...

As an alternative to the classical approach for analysing dichotomous choice environmental valuation data, this note develops a Bayesian approach by using the idea of Gibbs sampling and data augmentation. A by-product from the approach is a welfare measure, such as the mean willingness to pay,...

The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a...

Estimating limited dependent variable time series models through standard extremum methods can be a daunting computational task because of the need for integration of high order multiple integrals and/or numerical optimization of difficult objective functions. This paper proposes a classical...

A flexible Bayesian approach to a generalized linear model is proposed to describe the dependence of binary data on explanatory variables. The inverse of the exponential power cumulative distribution function is used as the link to the binary regression model. The exponential power family...