  The lack of interpretability and transparency are preventing economists from
using advanced tools like neural networks in their empirical research. In this
paper, we propose a class of interpretable neural network models that can
achieve both high prediction accuracy and interpretability. The model can be
written as a simple function of a regularized number of interpretable features,
which are outcomes of interpretable functions encoded in the neural network.
Researchers can design different forms of interpretable functions based on the
nature of their tasks. In particular, we encode a class of interpretable
functions named persistent change filters in the neural network to study time
series cross-sectional data. We apply the model to predicting individual's
monthly employment status using high-dimensional administrative data. We
achieve an accuracy of 94.5% in the test set, which is comparable to the best
performed conventional machine learning methods. Furthermore, the
interpretability of the model allows us to understand the mechanism that
underlies the prediction: an individual's employment status is closely related
to whether she pays different types of insurances. Our work is a useful step
towards overcoming the black-box problem of neural networks, and provide a new
tool for economists to study administrative and proprietary big data.
