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Table 3. Regression Analysis Result Of Pooled Two (2) Stage Least Squares Estimator Model for Dependent
Variable – Non-Performing Financing – (Model A, Model B, Model C and Model D)

Notes: This table provides the regression results of macroeconomic variables, macroprudential policy tools and institutional
factors with the Islamic bank’s Non- Performing Financing (model A, model B, model C and model D) using Pooled
Regression model and two-stage least square estimator model. ***,**,* significant at the 1%, 5% and 10% respectively.
Standard errors are given in parentheses.

The estimation regression results for the two-stage least square estimator model of dependent
variables non-performing financing (NPF) were reported in Tables 3. The adjusted R2 for models A,
B, C and D are 0.7133, 0.6578, 0.6146 and 0.6239 for the two-stage least square (2SLS) estimator.
Besides that, the p-value of F – statistic is at 0.00000. This result indicated that the independent
variables as a group are significant in determining the dependent variable at a 1% level of
significance.

From the regression result, 3 macroeconomic variables are significant influence Non-Performing
Financing. Firstly, the coefficient value of the natural logarithm of the balance of payment (BOP) was
significantly and uniformly positive for non-performing financing (NPF) at the 10% level. This result
showed that there was a positive and significant relationship between the balance of payment and non-
performing financing. This result is consistent with that of Kozlow (2003) based on his work entitled,
Selected Issues on the Treatment of Nonperforming Loans in Macroeconomic Statistics, pointed that
government deficit/surplus has a significant impact on Non-performing loan.

Secondly is credit growth where the coefficient value showed a negative significant relationship
between credit growth (CG) and non-performing financing. This result is in line with Boudriga,
Boulila and Jellouli (2009) who performed a study on the factors influencing nonperforming loans
and the potential impact of regulatory factors on credit risk exposure. They used aggregate banking,
financial, economic and legal environment panel data of 59 countries over the year of 2002 to 2006.
By using random effects panel regression analysis, the results indicated that credit growth rate has a
negative relationship with loan problems. Similarly, Klein (2013) has investigated the determinants

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