Generalized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to...

A new heteroskedastic hedonic regression model is suggested which takes into account time-varying volatility and is applied to a blue chips art market. A nonparametric local likelihood estimator is proposed, and this is more precise than the often used dummy variables method. The empirical...

Price indices for heterogenous goods such as real estate or fine art constitute crucial information for institutional or private investors considering alternative investments in times of financial markets turmoil. Classical mean-variance analysis of alternative investments has been hampered by...

Numerous studies on the drivers of brand extension success [Aaker and Keller, 1990, Broniarczyk and Alba, 1994, Hem et al., 2003, Völckner and Sattler, 2006] found evidence that parent-brand characteristics and the fit between parent brand and transfer product are the main and most influential...

The prices of wine is a key topic for market participants interested in valuing their stock, including dealers, restaurants or consumers who may be interested in optimizing their purchases. As a closely related issue, re-valuation is the need to regularly update the value of a stock. This need...

This paper presents insights on U.S. business cycle volatility since 1867 de- rived from diffusion indices. We employ a Bayesian dynamic factor model to obtain aggregate and sectoral economic activity indices. We find a remarkable increase in volatility across World War I, which is reversed...

This paper introduces a Divisia monetary aggregate for Germany and explores its information content for the Great Recession. Divisia money and the corresponding simple sum aggregate are highly correlated in normal times but begin to diverge before the crisis. Out of sample forecast analysis and...

In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector X can be represented as a sum of two components - a lowdimensional signal S and a noise component N. We show that this assumption...