  In this study, we develop nonparametric analysis of deviance tools for
generalized partially linear models based on local polynomial fitting. Assuming
a canonical link, we propose expressions for both local and global analysis of
deviance, which admit an additivity property that reduces to analysis of
variance decompositions in the Gaussian case. Chi-square tests based on
integrated likelihood functions are proposed to formally test whether the
nonparametric term is significant. Simulation results are shown to illustrate
the proposed chi-square tests and to compare them with an existing procedure
based on penalized splines. The methodology is applied to German Bundesbank
Federal Reserve data.
