  This paper presents an estimator for semiparametric models that uses a
feed-forward neural network to fit the nonparametric component. Unlike many
methodologies from the machine learning literature, this approach is suitable
for longitudinal/panel data. It provides unbiased estimation of the parametric
component of the model, with associated confidence intervals that have
near-nominal coverage rates. Simulations demonstrate (1) efficiency, (2) that
parametric estimates are unbiased, and (3) coverage properties of estimated
intervals. An application section demonstrates the method by predicting
county-level corn yield using daily weather data from the period 1981-2015,
along with parametric time trends representing technological change. The method
is shown to out-perform linear methods such as OLS and ridge/lasso, as well as
random forest. The procedures described in this paper are implemented in the R
package panelNNET.
