什么时候Google数据对临近预报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
摘要：
替代数据集与基于机器学习的工具一起广泛用于宏观经济即时预报。后者经常在没有完整了解其理论临近预报特性的情况下应用。在此背景下，本文提出了一种理论上接地nowcasting方法，允许研究人员将替代谷歌搜索数据（GSD）的预测，并结合有针对性的预选，岭正则化，广义交叉验证。打破与大多数现有的文献，它侧重于渐近的样本理论属性，我们建立了我们的方法的理论样本外的属性，并支持他们的蒙特卡罗模拟。我们将我们的方法应用于GSD来预测几个国家在不同经济时期的GDP增长率。我们的实证研究结果支持这样的观点，即GSD倾向于提高临近预报的准确性，即使在控制了官方变量之后，但在衰退时期和宏观经济稳定时期之间的收益是不同的。
英文摘要：
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.
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