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arXiv · 1702.06512

Semiparametric panel data models using neural networks

Andrew Crane-Droesch
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Abstract

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.

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Andrew Crane-Droesch. 2017-02-21. Semiparametric panel data models using neural networks. https://arxiv.org/abs/1702.06512

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