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

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  • Martha Banbura
  • Domenico Giannone
  • Michèle Lenza

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 othervariables, in dynamic systems. The algorithm is based on Kalman filtering methods andis computationally viable for large vector autoregressions (VAR) and dynamic factormodels (DFM). For a quarterly data set of 26 euro area macroeconomic and financialindicators, we show that both approaches deliver similar forecasts and scenario assessments.In addition, conditional forecasts shed light on the stability of the dynamicrelationships in the euro area during the recent episodes of financial turmoil and indicatethat only a small number of sources drive the bulk of the fluctuations in the euroarea economy.

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  • Martha Banbura & Domenico Giannone & Michèle Lenza, 2014. "Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections," Working Papers ECARES ECARES 2014-15, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/158499
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    Cited by:

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    2. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
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    9. Antonello D’Agostino & Michele Modugno & Chiara Osbat, 2017. "A Global Trade Model for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 1-34, December.
    10. Carlo Altavilla & Domenico Giannone & Michele Lenza, 2016. "The Financial and Macroeconomic Effects of the OMT Announcements," International Journal of Central Banking, International Journal of Central Banking, vol. 12(3), pages 29-57, September.
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    12. Jarociński, Marek & Bobeica, Elena, 2017. "Missing disinflation and missing inflation: the puzzles that aren't," Working Paper Series 2000, European Central Bank.
    13. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2017. "Common factors of commodity prices," Working Paper Series 2112, European Central Bank.
    14. Stefan Laseen & Marzie Taheri Sanjani, 2016. "Did the Global Financial Crisis Break the U.S. Phillips Curve?," IMF Working Papers 16/126, International Monetary Fund.
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    17. Clark, Todd E. & McCracken, Michael W., 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Paper 1413, Federal Reserve Bank of Cleveland.
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    23. Pestova, Anna & Mamonov, Mikhail, 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    24. Moral Carcedo, Julian & Perez García, Julian, 2015. "Feeding Large Econometric Models by a Mixed Approach of Classical Decomposition of Series and Dynamic Factor Analysis: Application to Wharton-UAM Model/Alimentando grandes modelos econométricos median," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 33, pages 487-512, Mayo.
    25. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.

    More about this item

    Keywords

    vector autoregression; bayesian shrinkage; dynamic factor model; conditional forecast; large cross-sections;

    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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