IDEAS home Printed from https://ideas.repec.org/a/aea/apandp/v112y2022p481-85.html
   My bibliography  Save this article

SVAR Identification from Higher Moments: Has the Simultaneous Causality Problem Been Solved?

Author

Listed:
  • José Luis Montiel Olea
  • Mikkel Plagborg-Møller
  • Eric Qian

Abstract

Two recent strands of the structural vector autoregression literature use higher moments for identification, exploiting either non-Gaussianity or heteroskedasticity. These approaches achieve point identification without exclusion or sign restrictions. We review this work critically and contrast its goals with the separate research program that has pushed for macroeconometrics to rely more heavily on credible economic restrictions. Identification from higher moments imposes stronger assumptions on the shock process than second-order methods do. We recommend that these assumptions be tested. Since inference from higher moments places high demands on a finite sample, weak identification issues should be given priority by applied users.

Suggested Citation

  • José Luis Montiel Olea & Mikkel Plagborg-Møller & Eric Qian, 2022. "SVAR Identification from Higher Moments: Has the Simultaneous Causality Problem Been Solved?," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 481-485, May.
    • Handle: RePEc:aea:apandp:v:112:y:2022:p:481-85
    DOI: 10.1257/pandp.20221047
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221047
    Download Restriction: no
    File URL: https://doi.org/10.3886/E167401V1
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221047.appx
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20221047.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/pandp.20221047?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fiorentini, Gabriele & Sentana, Enrique, 2023. "Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 643-665.
    2. Sentana, Enrique & Fiorentini, Gabriele, 2001. "Identification, estimation and testing of conditionally heteroskedastic factor models," Journal of Econometrics, Elsevier, vol. 102(2), pages 143-164, June.
    3. Lee, Adam & Mesters, Geert, 2024. "Locally robust inference for non-Gaussian linear simultaneous equations models," Journal of Econometrics, Elsevier, vol. 240(1).
    4. James H. Stock & Mark W. Watson, 2018. "Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments," Economic Journal, Royal Economic Society, vol. 128(610), pages 917-948, May.
    5. Adam Lee & Geert Mesters, 2021. "Robust non-Gaussian inference for linear simultaneous equations models," Economics Working Papers 1792, Department of Economics and Business, Universitat Pompeu Fabra.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
    2. Jarociński, Marek, 2024. "Estimating the Fed’s unconventional policy shocks," Journal of Monetary Economics, Elsevier, vol. 144(C).
    3. Davis, Richard & Ng, Serena, 2023. "Time series estimation of the dynamic effects of disaster-type shocks," Journal of Econometrics, Elsevier, vol. 235(1), pages 180-201.
    4. Geert Mesters & Piotr Zwiernik, 2022. "Non-Independent Components Analysis," Working Papers 1358, Barcelona School of Economics.
    5. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    6. Keweloh, Sascha A. & Hetzenecker, Stephan & Seepe, Andre, 2023. "Monetary policy and information shocks in a block-recursive SVAR," Journal of International Money and Finance, Elsevier, vol. 137(C).
    7. Fiorentini, Gabriele & Sentana, Enrique, 2023. "Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 643-665.
    8. Jetro Anttonen & Markku Lanne & Jani Luoto, 2024. "Statistically identified structural VAR model with potentially skewed and fat‐tailed errors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 422-437, April.
    9. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    10. Helmut Herwartz & Shu Wang, 2024. "Statistical identification in panel structural vector autoregressive models based on independence criteria," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 620-639, June.
    11. Herwartz, Helmut & Wang, Shu, 2023. "Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "Moment tests of independent components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 429-474, May.
    2. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    3. Thore Schlaak & Malte Rieth & Maximilian Podstawski, 2023. "Monetary policy, external instruments, and heteroskedasticity," Quantitative Economics, Econometric Society, vol. 14(1), pages 161-200, January.
    4. Sarah Arndt & Zeno Enders, 2023. "The Transmission of Supply Shocks in Different Inflation Regimes," CESifo Working Paper Series 10839, CESifo.
    5. Metiu, Norbert, 2021. "Anticipation effects of protectionist U.S. trade policies," Journal of International Economics, Elsevier, vol. 133(C).
    6. Régis Barnichon & Geert Mesters, 2020. "Optimal policy perturbations," Economics Working Papers 1716, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    8. Weber, Enzo, 2009. "Financial Contagion, Vulnerability and Information Flow: Empirical Identification," University of Regensburg Working Papers in Business, Economics and Management Information Systems 431, University of Regensburg, Department of Economics.
    9. Helmut Lütkepohl & Thore Schlaak, 2018. "Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 715-735, August.
    10. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    11. Klein, Roger & Vella, Francis, 2010. "Estimating a class of triangular simultaneous equations models without exclusion restrictions," Journal of Econometrics, Elsevier, vol. 154(2), pages 154-164, February.
    12. Prosper Dovonon, 2013. "Conditionally Heteroskedastic Factor Models With Skewness And Leverage Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1110-1137, November.
    13. Kastner, Gregor, 2019. "Sparse Bayesian time-varying covariance estimation in many dimensions," Journal of Econometrics, Elsevier, vol. 210(1), pages 98-115.
    14. Herwartz, Helmut & Lange, Alexander & Maxand, Simone, 2019. "Statistical identification in SVARs - Monte Carlo experiments and a comparative assessment of the role of economic uncertainties for the US business cycle," University of Göttingen Working Papers in Economics 375, University of Goettingen, Department of Economics.
    15. Yoon J. Jo & Sarah Zubairy, 2021. "State dependent government spending multipliers: Downward nominal wage rigidity and sources of business cycle fluctuations," Working Papers 20210127-001, Texas A&M University, Department of Economics.
    16. Elstner, Steffen & Grimme, Christian & Kecht, Valentin & Lehmann, Robert, 2022. "The diffusion of technological progress in ICT," European Economic Review, Elsevier, vol. 149(C).
    17. Kuhn, Moritz & Bartscher, Alina & Schularick, Moritz & Wachtel, Paul, 2021. "Monetary policy and racial inequality," CEPR Discussion Papers 15734, C.E.P.R. Discussion Papers.
    18. Ülkü, Numan & Weber, Enzo, 2013. "Identifying the interaction between stock market returns and trading flows of investor types: Looking into the day using daily data," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2733-2749.
    19. Fabio Fornari, 2002. "The size of the equity premium," Temi di discussione (Economic working papers) 447, Bank of Italy, Economic Research and International Relations Area.
    20. Mirela Sorina Miescu & Giorgio Motta & Dario Pontiggia & Raffaele Rossi, 2023. "The Expansionary Effects Of Housing Credit Supply Shocks," Working Papers 399832231, Lancaster University Management School, Economics Department.

    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
    • E00 - Macroeconomics and Monetary Economics - - General - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:apandp:v:112:y:2022:p:481-85. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.