This paper proposes a forecasting procedure involving a combination of MIDAS-type regression models, allowing one to use high frequency predictors with different sampling frequencies to predict the U.S. Federal Government Expenditures (Net Outlays), Revenues and Deficits all of which are low frequency (quarterly/annual) variables. Evidence shows that forecast combinations of MIDAS regression models provide forecast gains over the traditional models. It is also shown that one-step ahead real-time predictions from MIDAS regressions of annual expenditures and revenues have smaller forecast errors than one-step ahead projections provided by the Congressional Budget Office (CBO) for the fiscal years of 1998-2010. Moreover, MIDAS regressions with leads employed to have real-time forecast updates of the current quarter (nowcasting) federal expenditures and revenues are found to have improved forecast performance compared to MIDAS regressions without leads.
Note: Research papers posted on SSRN, including any findings, may differ from the final version chosen for publication in academic journals.