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A Narrative Approach to a Fiscal DSGE Model

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  • Thorsten Drautzburg

    (Federal Reserve Bank of Philadelphia)

Abstract

DSGE models are used for analyzing policy and the sources of business cycles. A competing approach uses VARs that are partially identified using, for example, narrative shock measures and are often viewed as imposing fewer restrictions on the data. Narrative shocks are identified non-structurally through information external to particular models. This uses non-structural narrative shock measures to inform the structural estimation of DSGE models. Since fiscal policy has received much recent attention but the foundations of the fiscal side of DSGE models are less well studied than their monetary building block, fiscal DSGE models are a particularly promising application. Preliminary results from a standard medium-scale DSGE model support this argument: Structurally identified monetary shocks line up well with narrative measures, whereas government spending shocks do not. Extending the model to include distortionary taxes and more general fiscal policy processes, I find that model implied labor tax shocks line up well with narrative tax shocks. Including different narrative shock measures affects parameter identification and implied measures such as fiscal multipliers.

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  • Thorsten Drautzburg, 2014. "A Narrative Approach to a Fiscal DSGE Model," 2014 Meeting Papers 791, Society for Economic Dynamics.
  • Handle: RePEc:red:sed014:791
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    2. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    3. Stéphane Lhuissier & Urszula Szczerbowicz, 2022. "Monetary Policy and Corporate Debt Structure," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 497-515, June.
    4. Mirela Miescu, 2019. "Uncertainty shocks in emerging economies," Working Papers 277077821, Lancaster University Management School, Economics Department.
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    7. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    8. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Waggoner, Daniel F., 2021. "Inference in Bayesian Proxy-SVARs," Journal of Econometrics, Elsevier, vol. 225(1), pages 88-106.
    9. Martin Bruns, 2019. "Proxy VAR Models in a Data-Rich Environment," Discussion Papers of DIW Berlin 1831, DIW Berlin, German Institute for Economic Research.
    10. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
    11. Roos, Michael W. M. & Reccius, Matthias, 2021. "Narratives in economics," Ruhr Economic Papers 922, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    12. Michael Roos & Matthias Reccius, 2021. "Narratives in economics," Papers 2109.02331, arXiv.org, revised Dec 2022.
    13. Gerald Carlino & Thorsten Drautzburg, 2020. "The role of startups for local labor markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 751-775, September.
    14. Martínez-Hernández, Catalina, 2020. "Disentangling the effects of multidimensional monetary policy on inflation and inflation expectations in the euro area," Discussion Papers 2020/18, Free University Berlin, School of Business & Economics.
    15. Jentsch, Carsten & Lunsford, Kurt G., 2016. "Proxy SVARs : asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States," Working Papers 16-10, University of Mannheim, Department of Economics.
    16. Thorsten Drautzburg & Jesus Fernandez-Villaverde & Pablo Guerrón-Quintana, 2017. "Political Distribution Risk and Aggregate Fluctuations," PIER Working Paper Archive 17-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Jul 2017.
    17. Juan Antolin-Diaz & Juan F. Rubio-Ramirez, 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta.
    18. Nikolaos Kokonas & Paulo Santos Monteiro, 2020. "The Ins and Outs of Unemployment in General Equilibrium," Discussion Papers 2014, Centre for Macroeconomics (CFM).
    19. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    20. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    21. Mirela S. Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 894, Queen Mary University of London, School of Economics and Finance.

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    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

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