We propose in this paper a Bayesian approach with a noninformative prior distribution developed in Mengersen and Robert (1996) and Robert and Titterington (1996) in the setup of mixtures of distributions and hidden Markov models, respectively.

The class of paremetric dynamic latent variable models is becoming more and more popular in economics and finance. Dynamic disequilibrium models, latent factor models, switching regimes models, stochastic volatility models are only few examples of this class of models. Inference in this calss...

A simulation method based on importance sampling, Gibbs and Metropolis-Hastings techniques allows to approximate the ratio between the likelihhod function computed for two different parameter values. Thus it is possible to approximate the maximum likelihood estimator in the general framework of...

Measuring and reducing energy consumption constitutes a crucial concern in public policies aimed at mitigating global warming. The real estate sector faces the challenge of enhancing building efficiency, where insights from experts play a pivotal role in the evaluation process. This research...

In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently, (ii) there is a low co-movement between the three E, S, G pillars and (iii) there are specific ESG Key Performance Indicators...

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying...

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative...