## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Series** & \begin{tabular}{c} **[PERSON], [PERSON] \& [PERSON]** \\ **Statistic/\(p\)-Value** \\ \end{tabular} & \begin{tabular}{c} **[PERSON], [PERSON] and [PERSON]** \\ **Statistic/\(p\)-Value** \\ \end{tabular} & \
\begin{tabular}{c} **ADF–Fisher Chi-Square** \\ **Statistic/\(p\)-Value** \\ \end{tabular} &
\begin{tabular}{c} **PP–Fisher Chi-Square** \\ **Statistic/\(p\)-Value** \\ \end{tabular} \\ \hline LOGATM & 6.86189 & 1.0000 & 5.11722 & 1.0000 & 25.026 & 0.9997 & 41.418 & 0.8952 \\ D(LOGATM) & \(-3.79458\) & 0.0001 & \(-2.34246\) & 0.0096 & 63.2403 & 0.1366 & 73.5263 & 0.0263 \\ LOGBANKEMPL & \(-1.39476\) & 0.0815 & 2.67976 & 0.9963 & 27.0677 & 0.9992 & 44.3244 & 0.8233 \\ D(LOGBANKEMPL) & \(-16.611\) & 0.0000 & \(-3.32252\) & 0.0004 & 73.8404 & 0.0377 & 77.9552 & 0.0181 \\ LOGEDSI\_HC & 0.56464 & 0.7138 & 4.20747 & 1.0000 & 14.2987 & 1.0000 & 16.0837 & 1.0000 \\ D(LOGCDSI\_HC) & \(-22.7581\) & 0.0000 & \(-5.99027\) & 0.0000 & 102.514 & 0.0001 & 129.715 & 0.0000 \\ LOGEDSI\_IDT & \(-1.70833\) & 0.0438 & 3.74306 & 0.9999 & 28.5285 & 0.9983 & 56.6257 & 0.3773 \\ D(LOGCDSI\_IDT) & \(-19.6752\) & 0.0000 & \(-6.88869\) & 0.0000 & 112.115 & 0.0000 & 126.631 & 0.0000 \\ LOGDIGINCL & \(-5.78302\) & 0.0000 & \(0.68373\) & 0.7529 & 48.7197 & 0.6776 & 85.9135 & 0.0037 \\ D(LOGDIGINCL) & \(-11.9798\) & 0.0000 & \(-3.86545\) & 0.0001 & 82.6333 & 0.0044 & 95.0081 & 0.0003 \\ LOGEBANK & \(-7.22874\) & 0.0000 & \(-0.2065\) & 0.4182 & 63.8147 & 0.1696 & 108.319 & 0.0000 \\ D(LOGEBANK) & \(-5.53938\) & 0.0000 & \(-4.68541\) & 0.0000 & 97.9074 & 0.0001 & 120.296 & 0.0000 \\ LOGEDP & \(-4.13119\) & 0.0000 & 0.82608 & 0.7956 & 46.8881 & 0.7428 & 70.9023 & 0.0612 \\ D(LOGGDP) & \(-6.49996\) & 0.0000 & \(-1.73962\) & 0.041 & 60.9259 & 0.2409 & 75.2483 & 0.0296 \\ LOGROE & \(-6.92928\) & 0.0000 & \(-0.70481\) & 0.2405 & 50.973 & 0.3575 & 59.2718 & 0.1276 \\ D(LOGROE) & \(-12.1483\) & 0.0000 & \(-3.313\) & 0.0000 & 65.208 & 0.0205 & 66.7249 & 0.0151 \\ RD & \(-6.62636\) & 0.0000 & 0.69738 & 0.7572 & 43.9194 & 0.8346 & 60.4366 & 0.2547 \\ DRD & \(-12.407\) & 0.0000 & \(-2.89915\) & 0.0019 & 75.0955 & 0.0304 & 95.2513 & 0.0005 \\ MOMENT1 & \(-25.9586\) & 0.0000 & \(-2.62642\) & 0.0043 & 61.232 & 0.0657 & 79.1544 & 0.0017 \\ D(MOMENT1) & \(-11.6008\) & 0.0000 & \(-3.33753\) & 0.0004 & 62.9049 & 0.0119 & 72.3037 & 0.0013 \\ MOMENT2 & \(-106.927\) & 0.0000 & \(-9.25402\) & 0.0000 & 55.2905 & 0.1639 & 60.2819 & 0.077 \\ D(MOMENT2) & \(-5.56651\) & 0.0000 & \(-1.1143\) & 0.1326 & 43.311 & 0.3319 & 41.9486 & 0.3864 \\ \hline \hline \end{tabular} Source: Author’s own calculation.  
\end{table} 
Table 8: Unit root tests. tics, reinforcing their stationary nature after differencing. This consistent rejection of the unit root hypothesis at the first difference indicates that these series are integrated of order one, I(1), implying that they become stationary only after undergoing first differencing. These findings have substantial implications for econometric analysis, particularly when estimating relationships between variables. The presence of unit roots at the level suggests that employing these variables in their original form could lead to spurious regressions, thereby compromising the validity of inference. Given that most series become stationary at first difference, models incorporating these variables should either utilize differenced data or employ cointegration techniques if long-run equilibrium relationships exist. The need for such transformations highlights the importance of stationarity tests in determining the appropriate methodological approach for empirical analysis. Thus, the unit root test results confirm that the majority of the examined macroeconomic and financial time series are non-stationary in their level form but become stationary after first differencing. This evidence supports the classification of these variables as I(1) processes, necessitating appropriate econometric adjustments to avoid misleading statistical inferences.