This paper demonstrates that the adaptive learning approach to modelling private sector expectations can be used as an equilibriumselection mechanism in a natural-rate monetary model with unemployment persistence. In particular, it is shown that only one of the two rational expectations...

We study adaptive learning in a monetary overlapping generations model with sticky prices and monopolistic competition for the case where learning agents observe current endogenous variables. Observability of current variables is essential for informational consistency of the learning setup with...

Robust learning experiments confront participants with structurally different decision environments which they encounter, furthermore, repeatedly. Since the decision format does not depend on the rules (of game), forward looking deliberation (the shadow of the future) can be detected by...

In a model with private information of the worker about her ability and unobservable effort choice, the role of public and private employment services is analyzed. The coexistence of an inefficient employment exchange and an efficient private agency may lead to optimal screening with first best...

In this paper we present an experiment on the false consensus effect. Unlike previous experiments, we provide monetary incentives for revealing the actual estimation of others' behavior. In each session and round sixteen subjects make a choice between two options simultaneously. Then they...

Theory in time series analysis is often developed in the context of finite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is...

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer...

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by outliers whose detection and removal are particularly...