The selection of the most profitable customers in a customer database for targeted activities is often done based on observed behaviour in the past. Consequently, databases arising from the responses to, for example, direct mailings in the past are not random samples. When not all heterogeneity...

Due to high and low volatility periods, time series of absolute returns experience temporary level shifts which differ in length and size. In this paper we modify the basic Censored Latent Effects Autoregressive [CLEAR] model, such that it can describe and forecast the location and size of such...

GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving...

A time series model is proposed that describes the day-of-the-week seasonality in returns as well as in volatility of the daily S&P 500 index. The model is a periodic autoregression with periodically integrated GARCH [PAR-PIGARCH]. With this statistically adequate model, positive (negative)...

We develop a panel model for regional house prices, for which both the cross-section and the time series dimension is large. The model allows for stochastic trends, cointegration, cross-equation correlations and, most importantly, latent-class clustering of regions. Class membership is fully...