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摘要翻译：
替代数据集和新的基于机器学习的工具一起被广泛用于宏观经济临近预报，这些工具往往在没有完整描述其理论临近预报特性的情况下被应用。在此背景下，本文提出了一种理论支持的临近预报方法，该方法允许在预测因子中加入谷歌搜索数据，并将目标预选、脊正则化和广义交叉验证相结合。与大多数现有文献集中于渐近的样本内理论性质不同，我们建立了我们的方法的理论样本外性质，并得到了蒙特卡罗模拟的支持。我们将我们的方法应用于GSD来预测不同国家在不同经济时期的GDP增长率。我们的实证结果支持这样一个观点，即即使在控制了官方变量之后，GSD也倾向于提高临近预报的准确性，但收益在衰退和宏观经济稳定时期之间是不同的。
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英文标题：
《When are Google data useful to nowcast GDP? An approach via
pre-selection and shrinkage》
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作者：
Laurent Ferrara and Anna Simoni
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
Alternative data sets are nowadays widely used for macroeconomic nowcasting together with new Machine Learning-based tools which often are applied without having a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically-funded nowcasting methodology allowing to incorporate alternative Google Search Data (GSD) among the predictors and combining targeted preselection, Ridge regularization and Generalized Cross Validation. Breaking with most of the existing literature that focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology, that are supported by Monte-Carlo simulations. We apply our methodology to GSD in order to nowcast GDP growth rate of different 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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PDF链接：
https://arxiv.org/pdf/2007.00273
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