We suggest to extend the stacking procedure for a combination of predictive densities, proposed by Yao, Vehtari, Simpson, and Gelman(2018), to a setting where dynamic learning occurs about features of predictive densities of possibly misspecified models. This improves the averaging process of...

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...

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...

Economic policy decisions are often informed by empirical economic analysis. While the decision-maker is usually only interested in good estimates of outcomes, the analyst is interested in estimating the model. Accurate inference on the structural features of a model, such as cointegration, can...

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by...

This discussion paper resulted in a publication in the <A href="http://people.few.eur.nl/hkvandijk/PDF/Bos_Mahieu_and_Van_Dijk_2000_JoAE_daily_exchange_rate.pdf">'Journal of Applied Econometrics'</A>, 2000, 15(6), 671-696.<P> We construct models which enable a decision-maker to analyze the implications oftypical timeseries patterns of daily exchange rates for currency risk management. Ourapproach is...</p></a>

This discussion paper resulted in a publication in the <I>International Journal of Forecasting</I> (2010). Vol. 26(2), 231-247.<P> An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures in a Bayesian framework. This consists of a new...</p></i>

The empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class ofvector autoregressive (VAR) processes. The linear VAR model is extendedto...

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...

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...