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Policy analysis using DSGE models: an introduction

Author

Listed:
  • Argia M. Sbordone
  • Andrea Tambalotti
  • Krishna Rao
  • Kieran Walsh

Abstract

Many central banks have come to rely on dynamic stochastic general equilibrium, or DSGE, models to inform their economic outlook and to help formulate their policy strategies. But while their use is familiar to policymakers and academics, these models are typically not well known outside these circles. This article introduces the basic structure, logic, and application of the DSGE framework to a broader public by providing an example of its use in monetary policy analysis. The authors present and estimate a simple New Keynesian DSGE model, highlighting the core features that this basic specification shares with more elaborate versions. They then apply the estimated model to study the sources of the sudden increase in inflation that occurred in the first half of 2004. One important lesson derived from this exercise is that the management of expectations can be a more effective tool for stabilizing inflation than actual movements in the policy rate. This result is consistent with the increasing focus on the pronouncements of central bankers regarding their future actions.

Suggested Citation

  • Argia M. Sbordone & Andrea Tambalotti & Krishna Rao & Kieran Walsh, 2010. "Policy analysis using DSGE models: an introduction," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 23-43.
  • Handle: RePEc:fip:fednep:y:2010:i:oct:p:23-43:n:v.16no.2
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    Citations

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    Cited by:

    1. Horacio A. Aguirre, 2011. "On the “Science” of Monetary Policy: Methodological Notes," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(64), pages 83-115, October -.
    2. Andrea Stella & James H. Stock, 2012. "A state-dependent model for inflation forecasting," International Finance Discussion Papers 1062, Board of Governors of the Federal Reserve System (U.S.).
    3. Alexander Lipton, 2016. "Modern Monetary Circuit Theory, Stability Of Interconnected Banking Network, And Balance Sheet Optimization For Individual Banks," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(06), pages 1-57, September.
    4. Roberto Mota Navarro & Hern'an Larralde Ridaura, 2016. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," Papers 1601.00229, arXiv.org, revised Jul 2016.
    5. Francisco Sáez & Fernando Alvarez & Jesús Morales & Giovanni Guedez, 2011. "Expectations, Inter-Sectorial Relationships and the Business Cycle," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(63), pages 97-147, July - Se.
    6. Wolters, Maik Hendrik, 2016. "How the Baby Boomers' Retirement Wave Distorts Model-Based Output Gap Estimates," Annual Conference 2016 (Augsburg): Demographic Change 145812, Verein für Socialpolitik / German Economic Association.
    7. Alexander Lipton, 2015. "Modern Monetary Circuit Theory, Stability of Interconnected Banking Network, and Balance Sheet Optimization for Individual Banks," Papers 1510.07608, arXiv.org.
    8. Marco Del Negro & Stefano Eusepi & Marc Giannoni & Argia M. Sbordone & Andrea Tambalotti & Matthew Cocci & Raiden B. Hasegawa & M. Henry Linder, 2013. "The FRBNY DSGE model," Staff Reports 647, Federal Reserve Bank of New York.
    9. Bas van Aarle, 2012. "Macroeconomic Fluctuations in a Stylized DSGE Model with Disequilibrium Dynamics," CESifo Working Paper Series 4017, CESifo Group Munich.
    10. Zhicheng Zhou & Prapatchon Jariyapan, 2013. "The impact of macroeconomic policies to real estate market in People's Republic of China," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 2(3), pages 75-92, September.
    11. Martin Seneca, 2010. "A DSGE model for Iceland," Economics wp50, Department of Economics, Central bank of Iceland.
    12. Kiss, Gábor Dávid & Kovács, György & Varga, János Zoltán, 2016. "Várakozások és a monetáris politika - különös tekintettel a magyarországi gyakorlatra
      [Expectations and monetary policy, with special attention to practice in Hungary]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1192-1216.
    13. Stefano Gurciullo, 2014. "Stess-testing the system: Financial shock contagion in the realm of uncertainty," Papers 1412.1679, arXiv.org.
    14. Sinitskaya, Ekaterina & Tesfatsion, Leigh, 2015. "Macroeconomies as constructively rational games," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 152-182.
    15. Sinitskaya, Ekaterina, 2014. "Computational modeling of an economy using elements of artificial intelligence," ISU General Staff Papers 201401010800005291, Iowa State University, Department of Economics.
    16. Jeroen Rozendaal & Yannick Malevergne & Didier Sornette, 2015. "Macroeconomic Dynamics of Assets, Leverage and Trust," Papers 1512.03618, arXiv.org.
    17. Sitthiyot, Thitithep, 2015. "Macroeconomic and Financial Management in an Uncertain World: What Can We Learn from Complexity Science?," MPRA Paper 73753, University Library of Munich, Germany, revised 11 Dec 2015.
    18. Clavero, Borja, 2017. "A contribution to the Quantity Theory of Disaggregated Credit," MPRA Paper 76657, University Library of Munich, Germany.
    19. repec:spr:jecstr:v:6:y:2017:i:1:d:10.1186_s40008-017-0070-2 is not listed on IDEAS
    20. Gani Ramadani, 2017. "Measuring wage and price stickiness using firm-level data and potential implications for monetary policy in Macedonia," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43 Bank for International Settlements.

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