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A review of nonfundamentalness and identification in structural VAR models

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  • Alessi, Lucia
  • Barigozzi, Matteo
  • Capasso, Marco
Abstract
We review, under a historical perspective, the development of the problem of nonfundamentalness of Moving Average (MA) representations of economic models. Nonfundamentalness typically arises when agents’ information space is larger than the econometrician’s one. Therefore it is impossible for the latter to use standard econometric techniques, as Vector AutoRegression (VAR), to estimate economic models. We restate the conditions under which it is possible to invert an MA representation in order to get an ordinary VAR and identify the shocks, which in a VAR are fundamental by construction. By reviewing the work by Lippi and Reichlin [1993] we show that nonfundamental shocks may be very different from fundamental shocks. Therefore, nonfundamental representations should not be ruled out by assumption and indeed methods to detect nonfundamentalness have been recently proposed in the literature. Moreover, Structural VAR (SVAR) can be legitimately used for assessing the validity of Dynamic Stochastic General Equilibrium models only if the representation associated with the economic model is fundamental. Factor models can be an alternative to SVAR for validation purposes as they do not have to deal with the problem of nonfundamentalness. JEL Classification: C32, C51, C52

Suggested Citation

  • Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A review of nonfundamentalness and identification in structural VAR models," Working Paper Series 922, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2008922
    Note: 1023254
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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. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 67-116.
    3. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2010. "Dynamic Macroeconomic Effects of Public Capital: Evidence from Regional Italian Data," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 69(1), pages 29-66, April.
    4. G. Fagiolo & A. Roventini, 2009. "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 6.
    5. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
    6. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    7. Féve, Patrick & Jidoud, Ahmat, 2012. "Identifying News Shocks from SVARs," Journal of Macroeconomics, Elsevier, vol. 34(4), pages 919-932.
    8. Johannes Hermanus Kemp, 2020. "Empirical estimates of fiscal multipliers for South Africa," WIDER Working Paper Series wp-2020-91, World Institute for Development Economic Research (UNU-WIDER).
    9. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    10. repec:zbw:bofrdp:2009_018 is not listed on IDEAS
    11. Gianluca Cubadda & Francesco Giancaterini & Alain Hecq & Joann Jasiak, 2023. "Optimization of the Generalized Covariance Estimator in Noncausal Processes," Papers 2306.14653, arXiv.org, revised Jan 2024.

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    More about this item

    Keywords

    dynamic stochastic general equilibrium models; factor models; Nonfundamentalness; structural VAR;
    All these keywords.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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