Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

Arbeitspapier

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. Both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy.

Object Details

• ISBN: 978-92-899-1141-2
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ECB Working Paper ; No. 1733

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Forecasting Models; Simulation Methods
• Subject: Bayesian shrinkage ; conditional forecast ; dynamic factor model ; large cross-sections ; vector autoregression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bańbura, Marta ; Giannone, Domenico ; Lenza, Michele
• Event: Veröffentlichung
• (who): European Central Bank (ECB)
• (where): Frankfurt a. M.
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/154166
• Last update: 10.03.2025, 11:41 AM CET