This paper proposes a simple alternative to existing panel unit root tests that allow for cross-section dependence. The proposed test involves augmenting standard Dickey-Fuller (DF) or Augmented Dickey-Fuller (ADF) regressions with cross-section averages of lagged levels and first differences of individual series. The asymptotic null distribution of the individual augmented CADF statistics and their averages are derived, showing that they are asymptotically similar and do not depend on factor loadings. However, they are asymptotically correlated due to their dependence on common factors. The paper also considers a truncated version of the CADF statistic to avoid the influence of extreme outcomes when the time series dimension \( T \) is small. Monte Carlo experiments are conducted to investigate the small sample properties of the proposed tests, demonstrating satisfactory size and power even for relatively small values of \( N \) and \( T \). The tests are particularly effective for models with incidental trends and high degrees of cross-section dependence. The paper concludes with a discussion of the implications of the results and future research directions.This paper proposes a simple alternative to existing panel unit root tests that allow for cross-section dependence. The proposed test involves augmenting standard Dickey-Fuller (DF) or Augmented Dickey-Fuller (ADF) regressions with cross-section averages of lagged levels and first differences of individual series. The asymptotic null distribution of the individual augmented CADF statistics and their averages are derived, showing that they are asymptotically similar and do not depend on factor loadings. However, they are asymptotically correlated due to their dependence on common factors. The paper also considers a truncated version of the CADF statistic to avoid the influence of extreme outcomes when the time series dimension \( T \) is small. Monte Carlo experiments are conducted to investigate the small sample properties of the proposed tests, demonstrating satisfactory size and power even for relatively small values of \( N \) and \( T \). The tests are particularly effective for models with incidental trends and high degrees of cross-section dependence. The paper concludes with a discussion of the implications of the results and future research directions.