[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/aah/create/2021-08.html
   My bibliography  Save this paper

Modelling and Estimating Large Macroeconomic Shocks During the Pandemic

Author

Listed:
  • Luisa Corrado

    (University of Rome Tor Vergata)

  • Stefano Grassi

    (University of Rome Tor Vergata and CREATES)

  • Aldo Paolillo

    (University of Rome Tor Vergata)

Abstract
This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014). Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector.

Suggested Citation

  • Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Modelling and Estimating Large Macroeconomic Shocks During the Pandemic," CREATES Research Papers 2021-08, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2021-08
    as

    Download full text from publisher

    File URL: https://repec.econ.au.dk/repec/creates/rp/21/rp21_08.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Levintal, Oren, 2017. "Fifth-order perturbation solution to DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 1-16.
    2. Sergey Ivashchenko, 2014. "DSGE Model Estimation on the Basis of Second-Order Approximation," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 71-82, January.
    3. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393, Elsevier.
    4. Amisano, Gianni & Tristani, Oreste, 2010. "Euro area inflation persistence in an estimated nonlinear DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1837-1858, October.
    5. 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.
    6. Andrew Binning & Junior Maih, 2015. "Sigma point filters for dynamic nonlinear regime switching models," Working Paper 2015/10, Norges Bank.
    7. Amisano, Gianni & Tristani, Oreste, 2011. "Exact likelihood computation for nonlinear DSGE models with heteroskedastic innovations," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2167-2185.
    8. Luca Fornaro & Martin Wolf, 2020. "Covid-19 coronavirus and macroeconomic policy," Economics Working Papers 1713, Department of Economics and Business, Universitat Pompeu Fabra.
    9. Christopher Otrok & Andrew Foerster & Alessandro Rebucci & Gianluca Benigno, 2017. "Estimating Macroeconomic Models of Financial Crises: An Endogenous Regime Switching Approach," 2017 Meeting Papers 572, Society for Economic Dynamics.
    10. 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.
    11. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    12. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    13. Matteo Iacoviello & Stefano Neri, 2010. "Housing Market Spillovers: Evidence from an Estimated DSGE Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 125-164, April.
    14. Robert Kollmann, 2015. "Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 239-260, February.
    15. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    16. Martin M. Andreasen, 2013. "Non‐Linear Dsge Models And The Central Difference Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 929-955, September.
    17. Dewachter, Hans & Wouters, Raf, 2014. "Endogenous risk in a DSGE model with capital-constrained financial intermediaries," Journal of Economic Dynamics and Control, Elsevier, vol. 43(C), pages 241-268.
    18. Greg Kaplan & Benjamin Moll & Giovanni L. Violante, 2020. "The Great Lockdown and the Big Stimulus: Tracing the Pandemic Possibility Frontier for the U.S," NBER Working Papers 27794, National Bureau of Economic Research, Inc.
    19. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    20. Andreasen, Martin M., 2011. "Non-linear DSGE models and the optimized central difference particle filter," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1671-1695, October.
    21. R Maria del Rio-Chanona & Penny Mealy & Anton Pichler & François Lafond & J Doyne Farmer, 2020. "Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(Supplemen), pages 94-137.
    22. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    23. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    24. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    25. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Juan F. Rubio-Ramirez & Martin Uribe, 2011. "Risk Matters: The Real Effects of Volatility Shocks," American Economic Review, American Economic Association, vol. 101(6), pages 2530-2561, October.
    26. Andreasen, Martin M., 2012. "An estimated DSGE model: Explaining variation in nominal term premia, real term premia, and inflation risk premia," European Economic Review, Elsevier, vol. 56(8), pages 1656-1674.
    27. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    28. Eichenbaum, Martin S. & Rebelo, Sergio & Trabandt, Mathias, 2022. "Epidemics in the New Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    29. R Maria del Rio-Chanona & Penny Mealy & Anton Pichler & François Lafond & J Doyne Farmer, 0. "Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective," Oxford Review of Economic Policy, Oxford University Press, vol. 36(Supplemen), pages 94-137.
    30. Eichenbaum, Martin & Rebelo, Sérgio & Trabandt, Mathias, 2022. "Epidemics in the Neoclassical and New-Keynesian Models," CEPR Discussion Papers 14903, C.E.P.R. Discussion Papers.
    31. Michael Woodford, 2022. "Effective Demand Failures and the Limits of Monetary Stabilization Policy," American Economic Review, American Economic Association, vol. 112(5), pages 1475-1521, May.
    32. Brinca, Pedro & Duarte, Joao B. & Faria-e-Castro, Miguel, 2021. "Measuring labor supply and demand shocks during COVID-19," European Economic Review, Elsevier, vol. 139(C).
    33. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.
    34. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    35. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    36. Garland Durham & John Geweke, 2014. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 1-44, Emerald Group Publishing Limited.
    37. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    38. Faria-e-Castro, Miguel, 2021. "Fiscal policy during a pandemic," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    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. Cardani, Roberta & Pfeiffer, Philipp & Ratto, Marco & Vogel, Lukas, 2023. "The COVID-19 recession on both sides of the Atlantic: A model-based comparison," European Economic Review, Elsevier, vol. 158(C).
    2. Melina, Giovanni & Villa, Stefania, 2023. "Drivers of large recessions and monetary policy responses," Journal of International Money and Finance, Elsevier, vol. 137(C).
    3. Cardani, Roberta & Croitorov, Olga & Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco & Vogel, Lukas, 2021. "The Euro Area's pandemic recession: A DSGE interpretation," JRC Working Papers in Economics and Finance 2021-10, Joint Research Centre, European Commission.
    4. Luca Portoghese & Patrizio Tirelli, 2024. "Getting ready for the next pandemic: supply- side policies to escape the health-vs-economy dilemma," DEM Working Papers Series 219, University of Pavia, Department of Economics and Management.
    5. Emanuele Colombo Azimonti & Luca Portoghese & Patrizio Tirelli, 2022. "Covid-19 supply-side fiscal policies to escape the health-vs-economy dilemma," DEM Working Papers Series 208, University of Pavia, Department of Economics and Management.
    6. Cardani, Roberta & Croitorov, Olga & Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco & Vogel, Lukas, 2022. "The euro area’s pandemic recession: A DSGE-based interpretation," Journal of Economic Dynamics and Control, Elsevier, vol. 143(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. Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Identifying Economic Shocks in a Rare Disaster Environment," CEIS Research Paper 517, Tor Vergata University, CEIS, revised 18 Jul 2024.
    2. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    3. Benchimol, Jonathan & Ivashchenko, Sergey, 2021. "Switching volatility in a nonlinear open economy," Journal of International Money and Finance, Elsevier, vol. 110(C).
    4. Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021. "Online estimation of DSGE models," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 33-58.
    5. Shah, Sayar Ahmad & Garg, Bhavesh, 2023. "Testing policy effectiveness during COVID-19: An NK-DSGE analysis," Journal of Asian Economics, Elsevier, vol. 84(C).
    6. Górajski, Mariusz & Kuchta, Zbigniew, 2024. "Are two financial frictions necessary to match U.S. business and financial cycles?," Finance Research Letters, Elsevier, vol. 59(C).
    7. Brinca, Pedro & Duarte, Joao B. & Faria-e-Castro, Miguel, 2021. "Measuring labor supply and demand shocks during COVID-19," European Economic Review, Elsevier, vol. 139(C).
    8. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    9. Melina, Giovanni & Villa, Stefania, 2023. "Drivers of large recessions and monetary policy responses," Journal of International Money and Finance, Elsevier, vol. 137(C).
    10. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    11. Hinterlang, Natascha & Moyen, Stephane & Röhe, Oke & Stähler, Nikolai, 2023. "Gauging the effects of the German COVID-19 fiscal stimulus package," European Economic Review, Elsevier, vol. 154(C).
    12. Ferrero, Andrea & Cesa-Bianchi, Ambrogio, 2021. "The Transmission of Keynesian Supply Shocks," CEPR Discussion Papers 16430, C.E.P.R. Discussion Papers.
    13. Pelin Ilbas, 2012. "Revealing the preferences of the US Federal Reserve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 440-473, April.
    14. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
    15. Cardani, Roberta & Pfeiffer, Philipp & Ratto, Marco & Vogel, Lukas, 2023. "The COVID-19 recession on both sides of the Atlantic: A model-based comparison," European Economic Review, Elsevier, vol. 158(C).
    16. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-31.
    17. Balduzzi, Pierluigi & Brancati, Emanuele & Brianti, Marco & Schiantarelli, Fabio, 2020. "The Economic Effects of COVID-19 and Credit Constraints: Evidence from Italian Firms' Expectations and Plans," IZA Discussion Papers 13629, Institute of Labor Economics (IZA).
    18. Waggoner, Daniel F. & Wu, Hongwei & Zha, Tao, 2016. "Striated Metropolis–Hastings sampler for high-dimensional models," Journal of Econometrics, Elsevier, vol. 192(2), pages 406-420.
    19. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2021. "Adaptive Importance Sampling for DSGE Models," BEMPS - Bozen Economics & Management Paper Series BEMPS84, Faculty of Economics and Management at the Free University of Bozen.
    20. Sanha Noh, 2020. "Posterior Inference on Parameters in a Nonlinear DSGE Model via Gaussian-Based Filters," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 795-841, December.

    More about this item

    Keywords

    COVID-19; Nonlinear; Non-Gaussian; Large shocks; DSGE;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:aah:create:2021-08. 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: the person in charge (email available below). General contact details of provider: http://www.econ.au.dk/afn/ .

    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.