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Francesca Monti
- 3 August 2026
- WORKING PAPER SERIES - No. 3266Details
- Abstract
- This paper studies the dynamics of U.S. sectoral producer prices in a large Bayesian Vector Auto Regression (BVAR) model where the Input-Output (IO) matrix is used to structure their long-run relationships. The model provides evidence of a sectoral spillover channel in driving headline inflation without imposing such a mechanism in the model’s structure. Forecasts of headline inflation have accuracy comparable to the Survey of Professional Forecasters’ and greater than those generated by a standard BVAR with the Minnesota prior, confirming that the IO matrix long-run prior conveys relevant information about the data. The study of an oil price shock shows that adding the production network prior alters the transmission of the shock, amplifying headline inflation. Across sectors, the peak price response to the oil shock increases with oil intensity. A narrowly sector-specific disturbance, such as a cereal price shock, has non-negligible aggregate effects once the production network is accounted for. Sectoral asymmetries are crucial for evaluating the macroeconomic consequences of macroeconomic shocks such as an energy price shock and a monetary policy shock, as industries with slower price adjustments amplify inflation persistence, even after the shock dissipates.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
- 12 February 2026
- WORKING PAPER SERIES - No. 3186Details
- 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 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.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
E63 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Comparative or Joint Analysis of Fiscal and Monetary Policy, Stabilization, Treasury Policy
H68 : Public Economics→National Budget, Deficit, and Debt→Forecasts of Budgets, Deficits, and Debt
- 12 August 2020
- WORKING PAPER SERIES - No. 2453Details
- Abstract
- Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three \Vs": the large number of time series continuously released (Volume), the complexity of the data covering various sectors of the economy, published in an asynchronous way and with different frequencies and precision (Variety), and the need to incorporate new information within minutes of their release (Velocity). In this paper, we explore alternative routes to bring Bayesian Vector Autoregressive (BVAR) models up to these challenges. We find that BVARs are able to effectively handle the three Vs and produce, in real time, accurate probabilistic predictions of US economic activity and, in addition, a meaningful narrative by means of scenario analysis.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C01 : Mathematical and Quantitative Methods→General→Econometrics
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods