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Robust Discovery of Regression Models
Econometrics and Statistics Pub Date : 2021-06-01 , DOI: 10.1016/j.ecosta.2021.05.004
Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry

Successful modeling of observational data requires jointly discovering the determinants of the underlying process and the observations from which it can be reliably estimated, given the near impossibility of pre-specifying both. To do so requires avoiding many potential problems, including substantive omitted variables; unmodeled non-stationarity and misspecified dynamics in time series; non-linearity; and inappropriate conditioning assumptions, as well as incorrect distributional shape combined with contaminated observations from outliers and shifts. The aim is to discover robust, parsimonious representations that retain the relevant information, are well specified, encompass alternative models, and evaluate the validity of the study. An approach is proposed that provides robustness in many directions. It is demonstrated how to handle apparent outliers due to alternative distributional assumptions; and discriminate between outliers and large observations arising from non-linear responses. Two empirical applications, utilizing datasets popularized in previous applications, show substantive improvements from the proposed approach to robust model discovery.



中文翻译:

回归模型的稳健发现

观测数据的成功建模需要共同发现潜在过程的决定因素和可以可靠地估计它的观测结果,因为几乎不可能预先指定两者。这样做需要避免许多潜在的问题,包括实质性遗漏变量;时间序列中未建模的非平稳性和错误指定的动态;非线性;和不适当的调节假设,以及不正确的分布形状与异常值和偏移的污染观察相结合。目的是发现保留相关信息、明确说明、包含替代模型并评估研究有效性的稳健、简约的表示。提出了一种在许多方向上提供鲁棒性的方法。演示了如何处理由于替代分布假设而导致的明显异常值;并区分由非线性响应引起的异常值和大量观察值。两个经验应用程序利用在以前的应用程序中普及的数据集,显示了从所提出的方法到稳健模型发现的实质性改进。

更新日期:2021-06-01
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