'We are observing the rapid growth of available complex data sets and structures that cannot be satisfactorily reconciled with the rigid and restrictive assumptions of traditional methods in statistics and Data Science. This is due to the existence of unstable, non-linear and complex interdependencies between groups and individuals and their heterogeneity. We develop statistical methods to describe and model these phenomena in the language of statistics. Subsequently, we use these as a basis to develop suitable prediction models and to evaluate their reliability.'
Professor Haupt's research focuses on the development of semi- and nonparametric regression and prediction models. In addition to theoretical research on the statistical properties of the developed methods, empirical work is being carried out on a wide range of applications and projects in the field of economics and business and other disciplines.
- Behm S., Haupt H. and A. Schmid  Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany, Spatial statistics, article in press.
- Haupt H., Schnurbus J. and W. Semmler , Estimation of grouped, time-varying convergence in economic growth, Econometrics and Statistics 8, 141–158.
- Scholz M., Schnurbus J., Haupt H., Dorner V., Landherr A. and F. Probst  Dynamic Effects of User- and Marketer-Generated Content on Consumer Purchase Behavior: Modeling the Hierarchical Structure of Social Media Websites, Decision Support Systems 113, 43–55.
- Schnurbus J., Haupt H. and V. Meier  Economic transition and growth – A replication, Journal of Applied Econometrics 32, 1039–1042.
- Fritsch M., Haupt H. and P.T. Ng  Urban house price surfaces near a World Heritage Site: modeling conditional price and spatial heterogeneity, Regional Science and Urban Economics 60, 260–275.
- Oberhofer, W. & Haupt, H. 2016. Asymptotic theory for nonlinear quantile regression under weak dependence. Econometric Theory, 32: 686–713.