Multi-Level Nowcasting: Estimation in a Post-COVID Landscape

Abstract

With the economic upheaval brought by COVID-19, it was very difficult to understand economic events as they unfolded in real time, during unprecedented pandemic conditions. Since existing methods did not adequately address the rapid changes to the economy on a statewide basis, we felt compelled to create a novel approach to (a) bring current critical data and (b) to evaluate the varying impacts of changes occurring in real time. As practitioners, this created actionable data to forecast future economic scenarios using public datasets and readily available spreadsheet software, giving guidance on an economy in conditions with no direct analogue. This paper describes a way to integrate public data to assess present economic changes and describes the approach using straightforward processes and accessible tools. By comparing and contrasting findings in Arizona and Colorado, our analysis of this approach reveals that the updated data showing the current state of the economy (the Nowcast) is a robust approach for creating accurate, current data, and the various methods for further dissection (the Multi-Level component) create informative datasets for a detailed analysis, with the caveat that structural changes to the economy need to be kept in mind so as not to confuse what is firmly known with what is an overly broad application of the method.

Keywords

• COVID-19
• Nowcasting
• regional economics
• supersectors