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VAR Information and the Empirical Validation of DSGE Models

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  • Forni, Mario
  • Gambetti, Luca
  • Sala, Luca

Abstract

A shock of interest can be recovered, either exactly or with a good approximation, by means of standard VAR techniques even when the structural MA representation is non- invertible. We propose a measure of how informative a VAR model is for a specific shock of interest. We show how to use such a measure for the validation of shocks' transmission mechanism of DSGE models through VARs. In an application, we validate a theory of news shocks. The theory does fairly well for all variables, but understates the long-run effects of technology news on TFP.

Suggested Citation

  • Forni, Mario & Gambetti, Luca & Sala, Luca, 2016. "VAR Information and the Empirical Validation of DSGE Models," CEPR Discussion Papers 11178, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:11178
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    Cited by:

    1. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    2. Iskrev, Nikolay, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Paper Series 2161, European Central Bank.
    3. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    4. Mario Forni & Luca Gambetti & Luca Sala, 2017. "News, Uncertainty and Economic Fluctuations (No News is Good News)," Center for Economic Research (RECent) 132, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    5. Bhattarai, Keshab & Mallick, Sushanta K. & Yang, Bo, 2021. "Are global spillovers complementary or competitive? Need for international policy coordination," Journal of International Money and Finance, Elsevier, vol. 110(C).
    6. Paul Beaudry & Patrick Feve & Alain Guay & Franck Portier, 2019. "When is Nonfundamentalness in SVARs a Real Problem?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 34, pages 221-243, October.
    7. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2019. "Information, VARs and DSGE Models," School of Economics Discussion Papers 1619, School of Economics, University of Surrey.
    8. Paul Levine & Joseph Pearlman & Alessio Volpicella & Bo Yang, 2022. "The Use and Mis-Use of SVARs for Validating DSGE Models," School of Economics Discussion Papers 0522, School of Economics, University of Surrey.
    9. G. Rigatos, 2021. "Statistical Validation of Multi-Agent Financial Models Using the H-Infinity Kalman Filter," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 777-798, October.
    10. Daniele Siena, 2017. "What's News in International Business Cycles," 2017 Meeting Papers 1206, Society for Economic Dynamics.

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    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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