As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or...

This paper addresses the growing gulf between traditional macroeconometrics and the increasingly dominant preference among macroeconomists to use DSGE models and to estimate them using Bayesian estimation with strong priors but not to test them as they are likely to fail conventional statistical...

We review recent findings in the application of Indirect Inference to DSGE models. We show that researchers should tailor the power of their test to the model under investigation in order to achieve a balance between high power and model tractability; this will involve choosing only a limited...

This study develops a framework for testing hypotheses on structural parameters in in-complete models. Such models make set-valued predictions and hence do not generally yield a unique likelihood function. The model structure, however, allows us to construct tests based on the least favorable...

In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for...