Addressing Missing Variables in OLS Regression: A Panel Data Approach

This research paper primarily utilizes Ordinary Least Squares (OLS) regression for testing. However, recognizing the potential issue of missing variables, this study has implemented a unique strategy to mitigate this challenge. Drawing inspiration from the approach employed by Jin Yu et al. (2018), the sample data has been structured into panel data, and the individual fixed effect model has been applied for testing. This methodology has been selected for its known effectiveness in addressing missing variables, thereby enhancing the reliability of the research findings.

Furthermore, it is notable that the correlation between the two tables has remained consistent throughout the testing process. This finding serves to further bolster the reliability of the research conclusions presented in this paper. By adopting a rigorous and meticulous approach to testing, this research has been able to generate valuable insights into the subject matter under investigation. It is hoped that these findings will contribute to the existing body of knowledge and pave the way for further research within this domain.