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Identifying News Shocks from SVARs

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  • Fève, Patrick
  • Jidoud, Ahmat

Abstract

This paper investigates the reliability of SVARs to identify the dynamic effects of news shocks. We show analytically that the dynamics implied by SVARs, using both long–run and short–run restrictions, are biased. However, the bias vanishes as long as news shocks account for most of the variability of the endogenous variable and the economy exhibits strong forward–looking behavior. Our simulation experiments confirm these findings and further suggest that the number of lags is a key ingredient for the success of the VAR setup. Furthermore, a simple correlation diagnostic test shows that news shocks identified using both restrictions are found to exhibit a correlation close to unity, provided that news shocks drive an overwhelming part of aggregate fluctuations.

Suggested Citation

  • Fève, Patrick & Jidoud, Ahmat, 2012. "Identifying News Shocks from SVARs," IDEI Working Papers 706, Institut d'Économie Industrielle (IDEI), Toulouse.
  • Handle: RePEc:ide:wpaper:25749
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    References listed on IDEAS

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    Cited by:

    1. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    2. Marco M. Sorge, 2013. "On the Fundamentalness of Nonfundamentalness in DSGE Models," CSEF Working Papers 340, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    3. Marco M. Sorge, 2013. "A Note on Information Flows and Identification of News Shocks Models," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 56(1), pages 28-38.
    4. Seymen, Atılım, 2013. "Sequential identification of technological news shocks," ZEW Discussion Papers 13-111, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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