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