Fiscal Monitoring with VARs

Jacopo Cimadomo, Domenico Giannone, Michele Lenza, Francesca Monti and Andrej Sokol

No 21160, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: 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 of 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 and a typical recession, finding a more muted response in the latter case.

Keywords: Nowcasting; Mixed-frequency; Monetary policy shock (search for similar items in EconPapers)
JEL-codes: C11 E52 E62 E63 H68 (search for similar items in EconPapers)
Date: 2026-02
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Working Paper: Fiscal monitoring with VARs (2026)
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