We estimate location values for single family houses using a standard house price and characteristics dataset and local polynomial regressions (LPR), a procedure that allows for complex interactions between the values of structural characteristics and the value of land. We also compare LPR to...

Im Zuge der Anstrengungen, die Verunsicherung der Verbraucher zu beheben, wird auf vielfältige Weise versucht, die Sicherheit von Lebensmitteln zu verbessern. Dabei ist auch der Informationsaustausch zwischen Akteuren in den Wertschöpfungsketten der Agrar- und Ernährungswirtschaft in den...

About thirty years ago Edward Leamer criticized the credibility of empirical research in economics. Since then there were huge improvements in research design, data collection and econometric methodology. On the other hand, the huge increase in computing power has increased the number of...

This article proposes a new class of rating scale models, which merges advantages and overcomes shortcomings of the traditional linear and ordered latent regression models. Both parametric and semi-parametric estimation is considered. The insights of an empirical application to satisfaction data...

Ordinary least squares regression residuals have a distribution that is dependent on a scale parameter. The term 'Studentization' is commonly used to describe a scale parameter dependent quantity U by a scale estimate S such that the resulting ratio, U/S, has a distribution that is free of from...

Marketers call for the examination of more complex functional structures of the links between service evaluation constructs. However, it seems that including and estimating non-additive effects in the structural equation modeling framework is a daunting task. Therefore, marketers need to be...

The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for joint inference on integrated volatility (IV), noise moments and price-noise relations. In the iid noise...

Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse...

Rating variables indicate the extent to which a quality is present, or absent, in a unit of observation. In this paper, we discuss a class of non-linear regression models for rating dependent variables and their estimation by parametric and semi-parametric methods. An application to life...

In this paper we present, propose and examine additional membership functions as also we propose least squares with genetic algorithms optimization in order to find the optimum fuzzy membership functions parameters. More specifically, we present the tangent hyperbolic, Gaussian and Generalized...