With Daniel Aaronson, Scott Brave, Michael Fogarty, Daniel W. Sacks, and Boyoung Seo.
Published in International Journal of Forecasting, 38(2), 2022: 567-581. https://doi.org/10.1016/j.ijforecast.2021.04.001Â
Abstract: 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 realtime 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.
The most recent version of the working paper is available as a pdf. (33 pages, 1092 KB)