预测回归

J. Gonzalo, Jean-Yves Pitarakis
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引用次数: 1

摘要

预测回归是一种广泛使用的计量经济学环境,用于使用一个或多个预测因子的过去值来评估经济和金融变量的可预测性。从业人员考虑的应用程序的性质通常涉及使用具有高度持久、平滑变化动态的预测器,而不是被预测的变量的嘈杂性。当使用没有明确认识到这一点和相关并发症的标准方法时,这种不平衡倾向于影响模型参数估计的准确性和关于它们的推断的有效性。越来越多的文献旨在引入专门设计用于在这种环境中产生准确推断的新技术。这些预测回归在应用工作中的频繁使用也导致从业者质疑在线性设置中观察可预测性的有效性,这种设置忽略了可预测性偶尔被关闭的可能性。这反过来又产生了一种新的研究流,旨在在预测回归中引入特定制度的行为,以便明确地捕捉诸如情景可预测性之类的现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Regressions
Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent, smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. This imbalance tends to affect the accuracy of the estimates of the model parameters and the validity of inferences about them when one uses standard methods that do not explicitly recognize this and related complications. A growing literature aimed at introducing novel techniques specifically designed to produce accurate inferences in such environments ensued. The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime-specific behavior within predictive regressions in order to explicitly capture phenomena such as episodic predictability.
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