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The COVID-19 shock and challenges for time series models

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  • Bobeica, Elena
  • Hartwig, Benny

Abstract

We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area inflation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. We illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability. JEL Classification: C53, E31, E37

Suggested Citation

  • Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20212558
    Note: 2382002
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    References listed on IDEAS

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    1. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    2. Luca Fornaro & Martin Wolf, 2020. "Covid-19 coronavirus and macroeconomic policy," Economics Working Papers 1713, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Martin Bodenstein & Giancarlo Corsetti & Luca Guerrieri, 2022. "Social distancing and supply disruptions in a pandemic," Quantitative Economics, Econometric Society, vol. 13(2), pages 681-721, May.
    4. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," Working Papers 20-26, Federal Reserve Bank of Philadelphia.
    5. Ascari, Guido & Fagiolo, Giorgio & Roventini, Andrea, 2015. "Fat-Tail Distributions And Business-Cycle Models," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 465-476, March.
    6. Eichenbaum, Martin S. & Rebelo, Sergio & Trabandt, Mathias, 2022. "Epidemics in the New Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    7. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    8. Frederic S. Mishkin, 2011. "Monetary Policy Strategy: Lessons from the Crisis," NBER Working Papers 16755, National Bureau of Economic Research, Inc.
    9. 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.
    10. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    12. David Baqaee & Emmanuel Farhi, 2022. "Supply and Demand in Disaggregated Keynesian Economies with an Application to the COVID-19 Crisis," American Economic Review, American Economic Association, vol. 112(5), pages 1397-1436, May.
    13. 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.
    14. Elena Bobeica & Marek Jarociński, 2019. "Missing Disinflation and Missing Inflation: A VAR Perspective," International Journal of Central Banking, International Journal of Central Banking, vol. 15(1), pages 199-232, March.
    15. Han, Chuan-Hsiang & Molina, German & Fouque, Jean-Pierre, 2014. "McMC estimation of multiscale stochastic volatility models with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 103(C), pages 1-11.
    16. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    17. 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.
    18. Magdalena Grothe & Aidan Meyler, 2018. "Inflation Forecasts: Are Market-Based and Survey-Based Measures Informative?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 171-188, January.
    19. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    20. 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.
    21. Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005. "Forecasting Using Relative Entropy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 383-401, June.
    22. Siddhartha Chib & Srikanth Ramamurthy, 2014. "DSGE Models with Student- t Errors," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 152-171, June.
    23. Shawn Ni & Dongchu Sun, 2005. "Bayesian Estimates for Vector Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 105-117, January.
    24. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    25. Veronica Guerrieri & Guido Lorenzoni & Ludwig Straub & Iván Werning, 2022. "Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?," American Economic Review, American Economic Association, vol. 112(5), pages 1437-1474, May.
    26. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    27. Hartwig, Benny, 2020. "Robust inference intime-varying structural VAR models: The DC-Cholesky multivariate stochasticvolatility model," Discussion Papers 34/2020, Deutsche Bundesbank.
    28. 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.
    29. Rebelo, Sérgio & Eichenbaum, Martin & Trabandt, Mathias, 2022. "Epidemics in the Neoclassical and New-Keynesian Models," CEPR Discussion Papers 14903, C.E.P.R. Discussion Papers.
    30. Primiceri, Giorgio & Lenza, Michele, 2020. "How to Estimate a VAR after March 2020," CEPR Discussion Papers 15245, C.E.P.R. Discussion Papers.
    31. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    32. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
    33. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    34. 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.
    35. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    36. Bobeica, Elena & Sokol, Andrej, 2019. "Drivers of underlying inflation in the euro area over time: a Phillips curve perspective," Economic Bulletin Articles, European Central Bank, vol. 4.
    37. Ciccarelli, Matteo & Osbat, Chiara, 2017. "Low inflation in the euro area: Causes and consequences," Occasional Paper Series 181, European Central Bank.
    38. Angelini, Elena & Darracq Pariès, Matthieu & Zimic, Srečko & Damjanović, Milan, 2020. "ECB-BASIR: a primer on the macroeconomic implications of the Covid-19 pandemic," Working Paper Series 2431, European Central Bank.
    39. O’Brien, Derry & Dumoncel, Clémence & Gonçalves, Eduardo, 2021. "The role of demand and supply factors in HICP inflation during the COVID-19 pandemic – a disaggregated perspective," Economic Bulletin Articles, European Central Bank, vol. 1.
    40. 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.
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    Cited by:

    1. Zeynep Kantur & Gülserim Özcan, 2022. "Dissecting Turkish inflation: theory, fact, and illusion," Economic Change and Restructuring, Springer, vol. 55(3), pages 1543-1553, August.
    2. Carlos Cañizares Martínez & Gabe J. de Bondt & Arne Gieseck, 2023. "Forecasting housing investment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 543-565, April.
    3. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2023. "Modelling Okun’s law: Does non-Gaussianity matter?," Empirical Economics, Springer, vol. 64(5), pages 2183-2213, May.
    4. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    5. Doojav, Gan-Ochir, 2021. "Socio-economic recovery from the Covid-19 pandemic: Macroeconomic impacts and policy issues in Mongolia," MPRA Paper 111197, University Library of Munich, Germany.
    6. Costin Radu Boldea & Bogdan Ion Boldea & Tiberiu Iancu, 2023. "The Pandemic Waves’ Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    7. Gan-Ochir Doojav, 2023. "Macroeconomic Effects of Covid-19 in a Commodity-Exporting Economy: Evidence from Mongolia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(5), pages 1323-1348, April.
    8. Haderer, Michaela, 2022. "An Estimated DSGE Model of the Euro Area with Expectations about the Timing and Nature of Liftoff from the Lower Bound," Working Papers 2022-05, University of Sydney, School of Economics.

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    More about this item

    Keywords

    COVID-19; forecasting; inflation; student's t errors; tilting; VAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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