@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}
}