Semiparametric panel data models using neural networks
Semiparametric panel data models using neural networks
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发表时间：
2017-02
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通讯作者：
A. Crane-Droesch
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作者：
A. Crane-Droesch
本文提出了一种半参数模型的估计器，它使用前馈神经网络来拟合非参数分量。与机器学习文献中的许多方法不同，这种方法适用于纵向/面板数据。它提供了模型参数分量的无偏估计，以及具有接近标称覆盖率的相关置信区间。仿真结果表明：（1）效率，（2）参数估计是无偏的，（3）估计区间的覆盖性质。应用部分通过使用1981-2015年期间的每日天气数据预测县级玉米产量来演示该方法，沿着的是代表技术变革的参数时间趋势。该方法被证明优于线性方法，如OLS和脊/套索，以及随机森林。本文所描述的过程是在R包panelNNET中实现的。
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.