Increasing dispersion in regression analysis means that with positive changes of the explanatory variable the residual variance increases. Motivated by theoretical questions in stability of demand systems we consider the question of increasing dispersion in a nonparameteric way. It amounts to...

Single index models are frequently used in econometrics and biometrics. Logit and probit models are special cases with fixed link functions. In this paper we consider a specification test that detects nonparametric deviations of the link function, e. g., testing against a semiparametric...

In this paper we investigate the gains of using nonparametric estimation methods in a family of models related to Generalised Linear Models. We focus especially on discrete choice models. We give an overview on different nonparametric and semiparametric approaches in this setting. In particular...

By extending the GARCH option pricing model of Duan (1995) to more flexible volatility es- timation it is shown that the prices of out-of-the-money options strongly depend on volatility features such as asymmetry. Results are provided for the properties of the stationary pricing distribution in...

In this note we review different approaches to non parametric regression. Kernel estimators are motivated from local averaging, solving ill-posed problems and weighting of binned data. Kernel estimators are compared to k-NN estimators and splines. The choice of smoothing paramet.er is discussed...