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Discussion Paper Details

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

Author(s): Marta Banbura, Domenico Giannone and Michele Lenza

Publication Date: April 2014

Keyword(s): Bayesian Shrinkage, Conditional Forecast, Dynamic Factor Model, Large Cross-Sections and Vector Autoregression

Programme Area(s): International Macroeconomics

Abstract: 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 vector autoregressions (VAR) and dynamic factor models (DFM). For a quarterly data set of 26 euro area macroeconomic and financial indicators, we show that 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.

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Bibliographic Reference

Banbura, M, Giannone, D and Lenza, M. 2014. 'Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=9931