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

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Listed author(s):
  • Giannone, Domenico
  • Bańbura, Marta
  • Lenza, Michele

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 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. JEL Classification: C11, C13, C33, C53

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Paper provided by European Central Bank in its series Working Paper Series with number 1733.

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Date of creation: Sep 2014
Handle: RePEc:ecb:ecbwps:20141733
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Keywords: Bayesian shrinkage; conditional forecast; dynamic factor model; large cross-sections; vector autoregression;

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Find related papers by JEL classification:
  • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
  • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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  1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
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