To accommodate the inhomogenous character of financial time series over longer time periods, standard parametric models can be extended by allow- ing their coeffcients to vary over time. Focusing on conditional heteroscedas- ticity models, we discuss various strategies to identify and estimate...

The Netherlands experienced a major increase in the number of jobs over the past decade.We show that the spectacular growth of the number of part-time jobs was an important reason for employment growth and the related decline in unemployment.

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian...

In this paper we confront sensitivity analysis with diagnostic testing.Every model is misspecified, but a model is useful if the parameters of interest (the focus) are not sensitive to small perturbations in the underlying assumptions. The study of the e ect of these violations on the focus is...

The binary-choice regression models such as probit and logit are used to describe the effect of explanatory variables on a binary response vari- able. Typically estimated by the maximum likelihood method, estimates are very sensitive to deviations from a model, such as heteroscedastic- ity and...

This paper looks at unobserved components models and examines the implied weighting pat- terns for signal extraction. There are three main themes. The first is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The...

We propose a class of distribution-free rank-based tests for the null hypothesis of a unit root. This class is indexed by the choice of a reference density g, which needs not coincide with the unknown actual innovation density f. The validity of these tests, in terms of exact finite sample size,...