@PhdThesis{macau_mods_00001722, author = {Hillmann, Benjamin}, title = {Inference in Predictive Regression Models with Persistent Regressors}, year = {2021}, publisher = {Christian-Albrechts-Universit{\"a}t zu Kiel}, address = {Kiel}, keywords = {Predictive Regression; Persisten Regressors; Tree Based Methods}, abstract = {This thesis comprises three papers on predictive regressions with persistent regressors. Standard approaches such as OLS suffer from second-order bias and corresponding test statistics show nonstandard limiting distributions in the presence of endogenous and persistent regressors. I discuss three different approaches to deal with this issue as well as further pitfalls such as nonlinearity and model uncertainty within this thesis. Two M-based tests are aggregated in Chapter 2, yielding a standard distribution for stable as well as persistent regressors. The setup is extended to nonlinear predictability and the performance of nonparametric and IV-based tests are compared in Chapter 3. Finally, the focus in Chapter 4 lies on multivariate predictions of different tree-based methods.}, url = {https://macau.uni-kiel.de/receive/macau_mods_00001722}, file = {:https://macau.uni-kiel.de/servlets/MCRFileNodeServlet/macau_derivate_00002826/Dissertation%20Hillmann%20Benjamin.pdf:PDF}, language = {en} }