Real-time Forecasts of State and Local Government Budgets with an Application to the COVID-19 Pandemic

Abstract

Using a sample of the 48 contiguous United States, we consider the problem of forecasting state and local governments' revenues and expenditures in real time using models that feature mixed-frequency data. We find that single-equation mixed data sampling (MIDAS) regressions that predict low-frequency fiscal outcomes using high-frequency economic data historically outperform both traditional fiscal forecasting models and theoretically motivated multi-equation models. We also consider an application of forecasting fiscal outcomes in the face of the economic uncertainty induced by the 2019-2020 coronavirus pandemic. Overall, we show that MIDAS regressions provide a simple tool for predicting fiscal outcomes in real time.

Keywords: Fiscal Policy, Forecasting, Mixed-Frequency Bayesian VAR, MIDAS Regressions

JEL Classification: C22, C32, C50, C53, E62