IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v37y2022i4p688-699.html
   My bibliography  Save this article

How to estimate a vector autoregression after March 2020

Author

Listed:
  • Michele Lenza
  • Giorgio E. Primiceri

Abstract

This paper illustrates how to handle a sequence of extreme observations—such as those recorded during the COVID‐19 pandemic—when estimating a vector autoregression, which is the most popular time‐series model in macroeconomics. Our results show that the ad hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it may underestimate uncertainty.

Suggested Citation

  • Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
  • Handle: RePEc:wly:japmet:v:37:y:2022:i:4:p:688-699
    DOI: 10.1002/jae.2895
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/jae.2895
    Download Restriction: no

    File URL: https://libkey.io/10.1002/jae.2895?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
    ---><---

    References listed on IDEAS

    as
    1. James H. Stock & Mark W. Watson, 2016. "Core Inflation and Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 770-784, October.
    2. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    3. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    4. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    6. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    7. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 823-866.
    8. Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2014. "Rare Shocks, Great Recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1031-1052, November.
    9. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2019. "Priors for the Long Run," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 565-580, April.
    10. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    11. Todd E. Clark & Francesco Ravazzolo, 2015. "Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 551-575, June.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    13. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    14. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    15. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    16. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    17. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
    18. Robert J. Barro & Jose F. Ursua, 2008. "Macroeconomic Crises since 1870," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 39(1 (Spring), pages 255-350.
    19. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    Full references (including those not matched with items on IDEAS)

    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. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    2. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    3. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    4. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    5. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    6. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    7. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    8. Benjamin K. Johannsen & Elmar Mertens, 2021. "A Time‐Series Model of Interest Rates with the Effective Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(5), pages 1005-1046, August.
    9. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    10. Ching-Wai Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "VAR Models with Non-Gaussian Shocks," Discussion Papers 1609, Centre for Macroeconomics (CFM).
    11. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    12. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    13. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    14. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    15. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    16. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    17. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    18. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
    19. Consolo, Agostino & Favero, Carlo A. & Paccagnini, Alessia, 2009. "On the statistical identification of DSGE models," Journal of Econometrics, Elsevier, vol. 150(1), pages 99-115, May.
    20. Belomestny, Denis & Krymova, Ekaterina & Polbin, Andrey, 2021. "Bayesian TVP-VARX models with time invariant long-run multipliers," Economic Modelling, Elsevier, vol. 101(C).

    More about this item

    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:wly:japmet:v:37:y:2022:i:4:p:688-699. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.