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A Better Model for Economic Forecasting During the Pandemic


As the U.S. began to shut down in mid-March in response to the coronavirus pandemic, economic forecasts were thrust into the spotlight. Second only to public health concerns, the question of what happens next to our jobs, businesses, and the economy at large have become all-consuming for a country on edge.


The pandemic presents unique problems when it comes to forecasting. As noted by FocusEconomics, a provider of economic analysis that surveys hundreds of economic experts from the leading banks and think tanks, the spread among economic growth forecasts for Q2 in the U.S. grew from 3.5 percentage points in early February to a staggering 56.8 percentage points by late April. While most economists expect to see a GDP rebound in the second half of the year, how comfortable can policy makers be with Q3 and Q4 projections given the divergence in Q2 forecasting? In late April, the Fed’s economists offered a baseline projection that showed Q3 and Q4 improvement accompanied with the caveat that “a more pessimistic projection was no less plausible than the baseline forecast.” Statements like this don’t exactly inspire confidence.


There is a monumental need for timely and reliable economic forecasts. In lieu of them, policymakers and businesses are increasingly turning to real time alternative data sources, like mobility data from Apple and Google, or credit card transaction data. While valuable, these data sources lack important context and connectivity to larger economic trends. (For example, credit card data does not capture the impact that this pandemic is having on cash transactions, and mobility data does not explain whether consumers are spending more or less when they reach their destinations.)


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