  The Generalized Additive Model (GAM) is a powerful tool and has been well
studied. This model class helps to identify additive regression structure. Via
available test procedures one may identify the regression structure even
sharper if some component functions have parametric form. The Generalized
Additive Partially Linear Models (GAPLM) enjoy the simplicity of the GLM and
the flexibility of the GAM because they combine both parametric and
nonparametric components. We use the hybrid spline-backfitted kernel estimation
method, which combines the best features of both spline and kernel methods for
making fast, efficient and reliable estimation under alpha-mixing condition. In
addition, simultaneous confidence corridors (SCCs) for testing overall trends
and empirical likelihood confidence region for parameters are provided under
independent condition. The asymptotic properties are obtained and simulation
results support the theoretical properties. For the application, we use the
GAPLM to improve the accuracy ratio of the default predictions for $19610$
German companies. The quantlets for this paper are available on
https://github.com.

Methodology