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Identification in structural vector autoregressive models with structural changes

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  • Emanuele BACCHIOCCHI

    ()

  • Luca FANELLI

    ()

Abstract

An increasing strand of the literature uses structural changes and different heteroskedasticity regimes found in the data constructively to improve the identification of structural parameters in Structural Vector Autoregressions (SVAR). A standard assumption in this literature is that the unconditional reduced form covariance matrix of the system varies while the structural parameters remain constant. With macroeconomic data this assumption appears untenable. This paper investigates the identification issues that arise in SVARs when structural breaks which occur at known dates affect both the reduced form unconditional covariance matrix and the structural parameters. It is shown that the combination of theory-driven restrictions with the knowledge that different heteroskedasticity regimes characterize the data generalize the necessary and sufficient identification conditions that hold for SVAR without breaks, opening interesting possibilities for practitioners. An empirical illustration shows the usef.ulness of the derived identification conditions by focusing on a small SVAR frequently used to investigate U.S. monetary policy. It is found that two heteroskedasticity regimes characterize the data before and after the 1980s, and this information is combined with economic reasoning to identify the effect of monetary policy shocks on output and inflation.

Suggested Citation

  • Emanuele BACCHIOCCHI & Luca FANELLI, 2012. "Identification in structural vector autoregressive models with structural changes," Departmental Working Papers 2012-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2012-16
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    File URL: http://wp.demm.unimi.it/files/wp/2012/DEMM-2012_016wp.pdf
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    Citations

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    Cited by:

    1. Helmut Lütkepohl & Aleksei Netsunajev, 2014. "Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market," Discussion Papers of DIW Berlin 1388, DIW Berlin, German Institute for Economic Research.
    2. Helmut Luetkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models," CESifo Working Paper Series 5308, CESifo Group Munich.
    3. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
    5. Helmut Herwartz & Martin Plödt, 2016. "Simulation Evidence on Theory-based and Statistical Identification under Volatility Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 94-112, February.
    6. Herwartz, Helmut & Plödt, Martin, 2014. "Sign restrictions and statistical identification under volatility breaks -- Simulation based evidence and an empirical application to monetary policy analysis," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100326, Verein für Socialpolitik / German Economic Association.
    7. Dmitry Kulikov & Aleksei Netsunajev, 2013. "Identifying monetary policy shocks via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2013-9, Bank of Estonia, revised 09 Dec 2013.
    8. Emanuele BACCHIOCCHI & Riccardo Jack LUCCHETTI, 2015. "Structure-Based SVAR Identification," Departmental Working Papers 2015-11, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

    More about this item

    Keywords

    Heteroskedasticity; Identi.cation; Monetary policy; Structural VAR;

    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
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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