Leveraging the increasing availability of ”big data” to inform forecasts of labor market activity is an active, yet challenging, area of research. Often, the primary difficulty is finding credible ways with which to consistently identify key elasticities necessary for prediction. To illustrate, we utilize a state-level event-study focused on the costliest hurricanes to hit the U.S. mainland since 2004 in order to estimate the elasticity of initial unemployment insurance (UI) claims with respect to search intensity, as measured by Google Trends. We show that our hurricane-driven Google Trends elasticity leads to superior real-time forecasts of initial UI claims relative to other commonly used models. Our approach is also amenable to forecasting both at the state and national levels, and is shown to be well-calibrated in its assessment of the level of uncertainty for its out-of-sample predictions during the Covid-19 pandemic.

利用越来越多的“大数据”来预测劳动力市场活动是一个活跃但具有挑战性的研究领域。通常，主要困难是找到可靠的方法来一致地确定预测所需的关键弹性。为了说明这一点，我们利用一项州级事件研究，重点关注自 2004 年以来袭击美国大陆的最昂贵的飓风，以估计初始失业保险 (UI) 索赔相对于搜索强度的弹性，由谷歌趋势衡量。我们表明，与其他常用模型相比，我们由飓风驱动的 Google 趋势弹性导致对初始 UI 声明的实时预测更出色。我们的方法也适用于州和国家层面的预测，