基于神经网络的半参数面板数据模型
Semiparametric panel data models using neural networks
摘要：
本文提出了一种利用前馈神经网络拟合非参数分量的半参数模型估计器。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以及代表技术变革的参数时间趋势来演示该方法。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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
