We evaluate various economic modelsí relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the modelsí relative performance can be varying over time. We show that the modelsí relative performance has,...

We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not...

In this study we suggest a Bayesian approach to fuzzy clustering analysis – the Bayesian fuzzy regression. Bayesian Posterior Odds analysis is employed to select the correct number of clusters for the fuzzy regression analysis. In this study, we use a natural conjugate prior for the...

We propose new information criteria for impulse response function matching estimators (IRFMEs). These estimators yield sampling distributions of the structural parameters of dynamic sto- chastic general equilibrium (DSGE) models by minimizing the distance between sample and theoretical impulse...