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Shock Restricted Structural Vector-Autoregressions

Author

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  • Sydney C. Ludvigson
  • Sai Ma
  • Serena Ng

Abstract

Identifying assumptions need to be imposed on autoregressive models before they can be used to analyze the dynamic effects of economically interesting shocks. Often, the assumptions are only rich enough to identify a set of solutions. This paper considers two types of restrictions on the structural shocks that can help reduce the number of plausible solutions. The first is imposed on the sign and magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and components of variables external to the autoregressive model. These non-linear inequality constraints can be used in conjunction with zero and sign restrictions that are already widely used in the literature. The effectiveness of our constraints are illustrated using two applications of the oil market and Monte Carlo experiments calibrated to study the role of uncertainty shocks in economic fluctuations.

Suggested Citation

  • Sydney C. Ludvigson & Sai Ma & Serena Ng, 2017. "Shock Restricted Structural Vector-Autoregressions," NBER Working Papers 23225, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23225 Note: AP EFG ME
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    References listed on IDEAS

    as
    1. Hyungsik Roger Moon & Frank Schorfheide & Eleonora Granziera & Mihye Lee, 2011. "Inference for VARs Identified with Sign Restrictions," NBER Working Papers 17140, National Bureau of Economic Research, Inc.
    2. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    3. repec:eee:macchp:v2-71 is not listed on IDEAS
    4. Antolin-Diaz, Juan & Rubio-Ramirez, Juan F., 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta, revised 01 Oct 2017.
    5. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    8. Antolin-Diaz, Juan & Rubio-Ramirez, Juan F., 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta, revised 01 Oct 2017.
    9. Hyungsik Roger Moon & Frank Schorfheide, 2012. "Bayesian and Frequentist Inference in Partially Identified Models," Econometrica, Econometric Society, vol. 80(2), pages 755-782, March.
    10. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, Elsevier.
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    Cited by:

    1. Ambrocio, Gene, 2017. "The real effects of overconfidence and fundamental uncertainty shocks," Research Discussion Papers 37, Bank of Finland.
    2. Ambrocio, Gene, 2017. "The real effects of overconfidence and fundamental uncertainty shocks," Research Discussion Papers 37/2017, Bank of Finland.
    3. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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