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

Eric Ghysels, Fotis Grigoris and Nazire Özkan

National Tax Journal, 2022, vol. 75, issue 4, 731 - 763

Abstract: Using a sample of the 48 contiguous US states, we consider the problem of forecasting state governments’ revenues and expenditures in real time using models that feature mixed-frequency data. We find that mixed-data sampling (MIDAS) regressions that predict low-frequency fiscal outcomes using high-frequency macroeconomic and financial market data outperform traditional fiscal forecasting models in both a relative and an absolute sense. We also consider an application of forecasting fiscal outcomes in the face of the economic uncertainty induced by the coronavirus pandemic. Overall, we show that MIDAS regressions provide a simple tool for predicting fiscal outcomes in real time.

Date: 2022
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