In this paper a robust data-driven procedure for decomposing seasonal time series based on a generalized Berlin Method (BV, Berliner Verfahren) as proposed by Heiler and Michels (1994) is discussed. The basic robust algorithm used here is an adaptation of the LOWESS (LOcally Weighted Scatterplot...

Prediction in time series models with a trend requires reliable estimation of the trend function at the right end of the observed series. Local polynomial smoothing is a suitable tool because boundary corrections are included implicitly. However, outliers may lead to unreliable estimates, if...

We analyze the impact of an individual's tendency to worry on willingness to pay (WTP) for a protective measure. We report on the results of a controlled experiment with real objects at stake. Worry was measured with the Worry Domains Questionnaire, an instrument determining an individual's...

The paper concerns the fixed-width confidence intervals for location based on M- estimators in the location model. A robust three-stage procedure is proposed and its asymptotic properties are studied. The performance of the procedure depends on some tuning parameters. Their effect on the...

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...

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and...

This paper examines monetary policy in Rudebusch and Svensson's (1999) two equation macroeconomic model when the policymaker recognizes that the model is an approximation and is uncertain about the quality of that approximation. It is argued that the minimax approach of robust control provides a...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters and then to predict the phenomenon under study at unobserved sites within the corresponding sampling...