## 4 Results
The _cross-section dependence tests_ (Table 9) reveal significant interdependencies among the panel data series, suggesting strong correlations across cross-sections. The results from the Breusch-Pagan LM test [125], Pesaran's scaled LM test, the bias-corrected LM test, and the Pesaran CD test [117] consistently reject the null hypothesis of no cross-section dependence at conventional significance levels. The Breusch-Pagan LM test statistics are notably large for all tested series, with \(p\)-values of 0.0000, confirming strong cross-sectional dependence. Similarly, [PERSON]'s scaled LM and its bias-corrected version yield highly significant statistics across all series, reinforcing the conclusion that cross-sectional units are interrelated. The Pesaran CD test, which is particularly informative in large panels, also consistently rejects the null hypothesis, further substantiating the presence of correlation among cross-sectional units. A particularly high degree of cross-section dependence is observed for variables such as LOGDESI_IDT, LOGDESI_HC, and LOGDIGINCL, where all test statistics reach exceptionally high values, indicating strong interlinkages between these series. LOGDGP presents a slightly different pattern, with the Pesaran CD test yielding a \(p\)-value of 0.0083, which, while still significant, suggests relatively lower cross-sectional dependence compared to other series. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Series**} & \multicolumn{2}{c}{**Breusch–Pagan LM**} & \multicolumn{2}{c}{**Pesaran Scaled LM**} & \multicolumn{2}{c}{**Bias-Corrected Scaled LM**} & \multicolumn{2}{c}{**Pesaran CD**} \\  & **Statistic/\(p\)-Value** & \multicolumn{2}{c}{**Statistic/\(p\)-Value**} & \multicolumn{2}{c}{**Statistic/\(p\)-Value**} & \multicolumn{2}{c}{**Statistic/\(p\)-Value**} \\ \hline LOGATM & 1261.915 & 0.0000 & 34.38028 & 0.0000 & 31.68028 & 0.0000 & 16.87518 & 0.0000 \\ LOGDANKEMPL & 1369.082 & 0.0000 & 38.42502 & 0.0000 & 35.72502 & 0.0000 & 16.76614 & 0.0000 \\ LOGDESI\_HC & 1647.333 & 0.0000 & 48.92693 & 0.0000 & 46.22693 & 0.0000 & 40.23152 & 0.0000 \\ LOGDESI\_IDT & 2106.091 & 0.0000 & 62.8448 & 0.0000 & 60.1448 & 0.0000 & 44.89023 & 0.0000 \\ LOGDIGINCL & 1352.775 & 0.0000 & 37.80957 & 0.0000 & 35.10957 & 0.0000 & 33.16456 & 0.0000 \\ LOGEBANK & 1442.69 & 0.0000 & 41.2032 & 0.0000 & 38.5032 & 0.0000 & 26.74926 & 0.0000 \\ LOGGDP & 774.432 & 0.0000 & 15.98141 & 0.0000 & 13.28141 & 0.0000 & 2.640705 & 0.0083 \\ LOGROE & NA & NA & NA & NA & NA & NA & NA & NA \\ MOMENT1 & NA & NA & NA & NA & NA & NA & NA \\ MOMENT2 & NA & NA & NA & NA & NA & NA & NA \\ RD & 899.6211 & 0.0000 & 20.70637 & 0.0000 & 18.00637 & 0.0000 & 12.91091 & 0.0000 \\ \hline \hline \end{tabular} Source: Author’s own calculation.  
\end{table} 
Table 9: Cross-section dependence tests. For some series, such as LOGROE, MOMENT1, and MOMENT2, test statistics are unavailable, indicating potential data limitations or an unbalanced panel structure that precluded the computation of meaningful results. These findings carry important econometric implications, particularly in terms of estimation techniques and model specification. The presence of cross-section dependence suggests that standard panel data estimators assuming independence across cross-sections may be inappropriate. Alternative approaches, such as common correlated effects estimators or spatial panel methods, should be considered to properly account for interdependencies. Furthermore, cross-section dependence may indicate the presence of common shocks or spillover effects across units, necessitating further investigation into potential underlying mechanisms driving these interrelations. The results obtained through the Method of Moments Quantile Regression (MMQR) (Table 10) provide a comprehensive analysis of the determinants of return on equity (ROE) across different quantiles, capturing heterogeneity in banking performance across EU countries.