Semiparametric panel data models using neural networks

TL;DR

This paper introduces a neural network-based estimator for semiparametric panel data models that provides unbiased parametric estimates and confidence intervals, demonstrating superior performance over traditional methods in simulations and real-world application.

Contribution

It develops a novel neural network approach for semiparametric panel data models that ensures unbiased parametric estimates and is suitable for longitudinal data analysis.

Findings

The method achieves unbiased parametric estimates with reliable confidence intervals.

It outperforms linear models and random forests in predicting county-level corn yield.

Simulations confirm efficiency and coverage properties of the estimator.

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.