Uncertainty in a Disaggregate Model: A Data Rich Approach Using Google Search Queries

Kalvinder K. Shields, T. Tran
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引用次数: 10

Abstract

This paper estimates the impact of uncertainty shocks in a disaggregate model featuring state-level unemployment and uncertainty, which is measured using Google search data. We show that the disaggregate model captures important spillover effects which a model using aggregate data would overlook resulting in significantly different peak responses and time dynamic effects. We find the effect of uncertainty shocks on state-level unemployment is recessionary and heterogeneous. The importance of national factors in propagating the effect of uncertainty is also heterogeneous across states, and overall less relevant than state-level factors. These heterogeneous effects are found to be related to state-specific industry compositions and the fiscal position.
分解模型中的不确定性:使用Google搜索查询的数据丰富方法
本文估计了不确定性冲击的影响,在一个分解模型,具有国家级失业和不确定性,这是用谷歌搜索数据测量。我们发现,分解模型捕捉到了重要的溢出效应,而使用聚合数据的模型会忽略这些溢出效应,从而导致显著不同的峰值响应和时间动态效应。我们发现不确定性冲击对州一级失业率的影响是衰退的和异质性的。国家因素在传播不确定性影响方面的重要性在各州之间也存在差异,总体上不如州一级因素相关。这些异质效应被发现与国家特定的产业构成和财政状况有关。
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