This paper contributes to the productivity literature by using results from firm-level productivity studies to improve forecasts of macro-level productivity growth. The paper employs current research methods on estimating firm-level productivity to build times-series components that capture the...

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...

A foundation is provided for a correct and accurate analysis of the relationship between monetary values and physical dimensions in economic production. A distinction is proposed between direct and indirect substitution. Linked to this, a classification is offered of various types of...

Nancy Cartwright views models as blueprints for nomological machines - machines which, if properly shielded, generate lawlike behaviour or regularities. Marcel Boumans has argued that we can look for devices inside models which enable us to measure aspects of these regularities. So, if models do...

We develop a method to screen for local cartels. We first test whether there is statistical evidence of clustering of outlets that score high on some characteristic that is consistent with collusive behavior. If so, we determine in a second step the most suspicious regions where further...

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

This discussion paper resulted in a chapter in: (K. Bocker (Ed.)) 'Rethinking Risk Measurement and Reporting - Volume II: Examples and Applications from Finance', 2010, London: Riskbooks.<P> This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of...</p>

Divergent priors are improper when defined on unbounded supports. Bartlett's paradox has been taken to imply that using improper priors results in ill-defined Bayes factors, preventing model comparison by posterior probabilities. However many improper priors have attractive properties that...

Highly non-elliptical posterior distributions may occur in several econometric models, in particular, when the likelihood information is allowed to dominate and data information is weak. We explain the issue of highly non-elliptical posteriors in a model for the effect of education on income...