Omay, TolgaAkdi, YilmazEmirmahmutoglu, FurkanEryilmaz, Meltem2025-01-052025-01-0520242227-739010.3390/math122234582-s2.0-85210727968https://doi.org/10.3390/math12223458https://hdl.handle.net/20.500.14411/10374The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.eninfo:eu-repo/semantics/openAccessPanel Unit Root TestCross-Sectional DependencyCommon Correlated Effect EstimatorCd TestC12C13C23ArticleQ1Q21222WOS:0013656243000010