论文标题
Google数据何时对GDP有用？通过预选和收缩的方法
When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage
论文作者
论文摘要
替代数据集被广泛用于宏观经济与基于机器学习的工具一起使用。后者通常在没有其理论现象属性的完整图片的情况下应用。在此背景下，本文提出了一种理论上扎根的现象方法，该方法使研究人员可以将替代性的Google搜索数据（GSD）纳入预测因子中，并结合了目标的预选，脊正规化和广义交叉验证。与大多数现有文献有关，重点关注渐近的样本内理论特性，我们建立了我们方法论的理论外部样本外特性，并通过蒙特卡洛模拟来支持它们。我们将我们的方法应用于GSD，以在各个经济时期内将几个国家的GDP增长率现实。我们的经验发现支持了这样一种观念，即即使控制官方变量，GSD也倾向于提高现状的准确性，但是收益在衰退期和宏观经济稳定性之间也有所不同。
Alternative data sets are widely used for macroeconomic nowcasting together with machine learning--based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them by Monte-Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.
