Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Regularized Instrumental Variable Regression |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 30 |
| Date | 2020 |
| Abstract Text | In econometrics, the instrumental variables (IV) regression is an alternative estimation method to ordinary least squares (OLS) if a regressor is potentially correlated with the error terms. However, the literature has revealed that IV estimators, especially the two stage least squares (2SLS) estimator, su er from bias when the number of instruments is large compared to the sample size. As shown in previous studies, this so called many instru- ments problem can be addressed by using regularization methods such as ridge or lasso. The goal of this study was to compute regularized 2SLS estimators and to determine whether these estimators have an advantage over standard 2SLS and OLS. A Monte Carlo simulation demonstrates that a regularization of the 2SLS estimator helps reduce the many instruments problem in many scenarios. This is especially the case when the number of instruments is near the sample size. In particular, a regularization of 2SLS based on the lasso was useful. These results are consistent with the literature. Furthermore, this work is an addition to already existing studies as it tried other parameter values in the Monte Carlo simulation. |
| Export |
BibTeX
|