PT Unknown
AU Hillmann, B
TI Inference in Predictive Regression Models with Persistent Regressors
PY 2021
PU Christian-Albrechts-Universität zu Kiel
WP https://macau.uni-kiel.de/receive/macau_mods_00001722
LA en
DE Predictive Regression; Persisten Regressors; Tree Based Methods
AB 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.
PI Kiel
ER