Z p erm 00568 0045 126 0208 03315 0262 126 0207

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Z P ERM 0.0568 0.045 1.26 0.208 0.3315 0.262 1.26 0.207 ddLOGSI ZE -0.7491 0.160 -4.67 0.000 0.1863 0.340 0.55 0.584 ddROA -0.2463 0.337 -0.73 0.466 -0.6565 0.543 -1.21 0.227 dGD 0.2738 0.195 1.40 0.161 -0.1779 0.256 -0.69 0.488 ddLEV -0.8647 0.412 -2.10 0.036 0.0506 0.811 0.06 0.950 PS 0.0013 0.000 2.81 0.005 -0.0010 0.003 -0.34 0.732 GS -0.0628 0.124 -0.51 0.613 -0.7094 0.424 -1.67 0.095 Constant -0.0996 0.034 -2.89 0.004 1.9636 0.311 6.30 0.000 Number of Observation: 165 Number of Groups: 33 P = 0.0000 R 2 (within) = 0.19 Wald x 2 =37.15 Number of Observation: 165 Number of Groups: 33 P = 0.0000 R 2 (within) = 0.10 Wald x 2 = 11.25 Note: The term “ d indicates the difference between the relevant variable. The validity of t and F statistics, R 2 and confidence intervals are affected if there is heteroscedasticity, autocorrelation and correlation between units. Therefore, if the model has at least one of variance, autocorrelation and correlation between units, resistant predictors should be used (Yerdelen Tatoğlu, 2013: 24 2). Since the critical values were exceeded in the tests for the heteroscedasticity, autocorrelation and correlation between units assumptions regarding the models used in the study, standard error-resisting prediction models were used which gave more consistent results considering these assumptions. It is common to rely on durable standard errors to provide valid statistical inferences when the assumptions of the basic regression model are violated. The most common of the alternative
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