000K utf8 1100 $c2021 1500 eng 2050 urn:nbn:de:gbv:8:3-2021-00635-0 3000 Hillmann, Benjamin 4000 Inference in Predictive Regression Models with Persistent Regressors$hChristian-Albrechts-Universität zu Kiel [Hillmann, Benjamin] 4030 Kiel$nChristian-Albrechts-Universität zu Kiel 4209 This thesis comprises three papers on predictive regressions with persistent regressors. Standard approaches such as OLS suffer from second-order bias and corresponding test statistics show nonstandard limiting distributions in the presence of endogenous and persistent regressors. I discuss three different approaches to deal with this issue as well as further pitfalls such as nonlinearity and model uncertainty within this thesis. Two M-based tests are aggregated in Chapter 2, yielding a standard distribution for stable as well as persistent regressors. The setup is extended to nonlinear predictability and the performance of nonparametric and IV-based tests are compared in Chapter 3. Finally, the focus in Chapter 4 lies on multivariate predictions of different tree-based methods. 4950 https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2021-00635-0$xR$3Volltext$534 4961 https://macau.uni-kiel.de/receive/macau_mods_00001722 5051 310 5051 330 5550 Persisten Regressors 5550 Predictive Regression 5550 Tree Based Methods