CORE DP 2026 / 16

Fiscal monitoring with VARs / Jacopo Cimadomo, Domenico Giannone, Michele Lenza, Francesca Monti, Andrej Sokol 

> We design a Bayesian Mixed-Frequency Vector Autoregression (VAR) model for fiscal monitoring, i.e., to nowcast the government deficit-to-GDP ratio in real time and provide a narrative for its dynamics. The model incorporates both monthly cash and quarterly accrual fiscal indicators, together with other high-frequency macroeconomic and financial variables, as well as real GDP and the GDP deflator. Our model produces timely monthly density nowcasts of the annual deficit ratio, while governments and official institutions generally only publish their point predictions bi-annually. Based on a database of real-time vintages of macroeconomic, financial, and fiscal variables for Italy, we show that the nowcasts of the annual deficit-to-GDP ratio produced by our model are similarly or more accurate than those of the European Commission, depending on the month in which the nowcast is produced. Our scenario analysis compares the dynamics of the deficit ratio associated with a monetary policy shock and a typical recession, finding a more muted response in the latter case.