  We consider a semiparametric generalized linear model and study estimation of
both marginal and quantile effects in this model. We propose an approximate
maximum likelihood estimator, and rigorously establish the consistency, the
asymptotic normality, and the semiparametric efficiency of our method in both
the marginal effect and the quantile effect estimation. Simulation studies are
conducted to illustrate the finite sample performance, and we apply the new
tool to analyze a Swiss non-labor income data and discover a new interesting
predictor.
