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

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  • 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
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    1. 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.
    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. 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.
    4. Lee, Adam & Mesters, Geert, 2024. "Locally robust inference for non-Gaussian linear simultaneous equations models," Journal of Econometrics, Elsevier, vol. 240(1).
    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.
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    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. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
    5. Carriero, Andrea & Marcellino, Massimiliano & Tornese, Tommaso, 2024. "Blended identification in structural VARs," Journal of Monetary Economics, Elsevier, vol. 146(C).
    6. 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).
    7. Ahmed, Rashad & Rebucci, Alessandro, 2024. "Dollar reserves and U.S. yields: Identifying the price impact of official flows," Journal of International Economics, Elsevier, vol. 152(C).
    8. Piotr Zwiernik & Geert Mesters, 2022. "Non-Independent Components Analysis," Working Papers 1358, Barcelona School of Economics.
    9. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    10. Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2024. "Specification tests for non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 244(2).
    11. Rogers, John & Sun, Bo & Wu, Wenbin, 2025. "Drivers of the global financial cycle," Journal of International Economics, Elsevier, vol. 156(C).
    12. Ryan, Michael & Holmes, Mark J., 2025. "The effect of uncertainty on output: Instruments, identification, and the role of investment," Economic Modelling, Elsevier, vol. 152(C).
    13. Alain Guay & Dalibor Stevanovic, 2026. "A spectral framework for non-gaussian SVARs," CIRANO Working Papers 2026s-02, CIRANO.
    14. 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.
    15. Lukas Hoesch & Adam Lee & Geert Mesters, 2024. "Locally robust inference for non‐Gaussian SVAR models," Quantitative Economics, Econometric Society, vol. 15(2), pages 523-570, May.
    16. 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.
    17. 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).
    18. 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.

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

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