We develop a new class of prior distributions for Bayesian comparison of nested models, which we call intrinsic moment priors, by combining the well-established notion of intrinsic prior with the recently introduced idea of non-local priors, and in particular of moment priors. Specifically, we...

The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, prior distributions on the parameter space of each candidate model require special care. While it is well known that improper priors cannot be used routinely for Bayesian model comparison, we claim...

Standard income inequality indices can be interpreted as a measure of welfare loss entailed in departures from equality of outcomes, for egalitarian social welfare functions defined on the distribution of outcomes. But such a welfare interpretation has been criticized for a long time on the...

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient, dependencies among the measurements have to be accounted for. For this the autoregressive ordinal...

This Paper describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational e?ort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing...

In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to...

In this paper, we make use of state space models toinvestigate the presence of stochastic trends in economic time series. Amodel is specified where such a trend can enter either in the autoregressiverepresentation or in a separate state equation. Tests based on the formerare analogous to...

Strategic choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. A comparative analysis is presented of possible advantages and limitations of different simulation...

This discussion paper led to a publication in 'Computational Statistics & Data Analysis' 56(11), pp. 3398-1414.Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior...

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...