Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach

Abstract

This paper proposes a real-time forecasting procedure involving a combination of MIDAS-type regression models constructed with predictors of different sampling frequencies for predicting the annual U.S. federal government current expenditures and receipts. The evidence shows that forecast combinations of MIDAS regression models provide forecast gains over traditional models, which suggests the use of mixed frequency data consisting of fiscal series and macroeconomic indicators for forecasting the annual federal budget. It is also shown that, although this was not statistically significant, MIDAS regressions with quarterly leads that are employed to include real-time forecast updates of the current year federal expenditures and receipts are found to have improved forecast performances compared to MIDAS regressions without leads.