Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections

By Martha Banbura, Domenico Giannone, M. Lenza. Social Science Research Network, 2015

169 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/69d701b7e66d06d469bae30f7f2b20b041320671

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Citations in the corpus

What's Up with the Phillips Curve? (2020, unknown): “Speci cally, we compute the responses of all variables as the di erence between their forecast conditional on a speci c path of unemployment and their unconditional forecast, following the methodology of Banbura et al. (2015).”
Common Factors of Commodity Prices † (2018, background): “Peersman (2013), The Role of Time-Varying Price Elasticities in Accounting for Volatility Changes in the Crude Oil Market , Journal of Applied Econometrics 28(7): 1087-1109.”