The objective of the proposed research project is the development of new statistical methods for nonparametric analysis of endogenous data. Nonparametric methodology has gained considerable importance since, due to technical progress, increasingly larger data sets are available in econometrics. On the other hand, it is often desirable to model economic relationships in an endogenous way to account for unobservable causal structures. The research project is divided into the following parts: The first goal is to develop a testing procedure to the detect endogenous selection in case of missing data. The test is based on instruments which are correlated to the data but not to the selection mechanism. In particular, an application of this method to survey data sets is planned. The second aim is to develop new nonparametric test and estimation methods in endogenous regression models with instruments. Here, we pursue an optimal estimator for partial information of the structural relationship such as its average value. In addition, we want to develop a testing procedure to justify model simplification, which is completely data driven and does not rely on unknown population parameters. Finally, we focus on the development of confidence bands for the graphical illustration of the estimated structural relationship with its statistical error.
DFG Programme
Research Fellowships
International Connection
USA
