  In this paper, we investigate a semiparametric regression model under the
context of treatment effects via a localized neural network (LNN) approach. Due
to a vast number of parameters involved, we reduce the number of effective
parameters by (i) exploring the use of identification restrictions; and (ii)
adopting a variable selection method based on the group-LASSO technique.
Subsequently, we derive the corresponding estimation theory and propose a
dependent wild bootstrap procedure to construct valid inferences accounting for
the dependence of data. Finally, we validate our theoretical findings through
extensive numerical studies. In an empirical study, we revisit the impacts of a
tightening monetary policy action on a variety of economic variables, including
short-/long-term interest rate, inflation, unemployment rate, industrial price
and equity return via the newly proposed framework using a monthly dataset of
the US.
