Alternative data are timely but messy. They can provide policymakers with real-time information, but their use is constrained by the complexity of their relationship with official statistics. Data from credit card transactions, search engines, or traffic have been made available since only recently, which makes it more difficult to precisely gauge their relationship with national accounts. This paper aims at solving this problem by compensating their short history with their large country coverage. It introduces a heterogeneous panel model approach where a neural network learns the relationship between Google Trends data and GDP growth from data pooled from 46 countries. The resulting “OECD Weekly Tracker” yields real-time estimates of weekly GDP, which are proven to be accurate using forecast simulations. It is a valuable compass for policymaking in turbulent waters.

替代数据及时但混乱。它们可以为政策制定者提供实时信息，但其使用受到与官方统计数据关系的复杂性的限制。来自信用卡交易、搜索引擎或流量的数据直到最近才开始提供，这使得精确衡量它们与国民账户的关系变得更加困难。本文旨在通过用其广泛的国家覆盖范围来弥补其短暂的历史来解决这个问题。它引入了一种异构面板模型方法，其中神经网络从 46 个国家汇总的数据中学习 Google 趋势数据与 GDP 增长之间的关系。由此产生的“经合组织每周追踪”产生每周 GDP 的实时估计，通过预测模拟证明这是准确的。它是动荡水域中决策的宝贵指南针。