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Monetary Policy Forecasting In A Dsge Model With Data That Is Uncertain, Unbalanced And About The Future

  • Andrés González Gómez

    ()

  • Lavan Mahadeva

    ()

  • Diego Rodríguez

    ()

  • Luis Eduardo Rojas

    ()

If theory-consistent models can ever hope to forecast well and to be useful for policy, theyhave to relate to data which though rich in information is uncertain, unbalanced and sometimes forecastsfrom external sources about the future path of other variables. One example from many is financial marketdata, which can help but only after smoothing out irrelevant short-term volatility. In this paper we proposecombining different types of useful but awkward data set with a linearised forward-looking DSGE modelthrough a Kalman Filter fixed-interval smoother to improve the utility of these models as policy tools. Weapply this scheme to a model for Colombia.

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Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 005480.

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Length: 35
Date of creation: 21 Apr 2009
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Handle: RePEc:col:000094:005480
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  1. Frank Schorfheide & Keith Sill & Maxym Kryshko, 2009. "DSGE Model-Based Forecasting of Non-modelled Variables," NBER Working Papers 14872, National Bureau of Economic Research, Inc.
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  3. Svensson, Lars E. O. & Woodford, Michael, 2000. "Indicator variables for optimal policy," Working Paper Series 0012, European Central Bank.
  4. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
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  6. Gunter Coenen & Andrew Levin & Volker Wieland, 2001. "Data uncertainty and the role of money as an information variable for monetary policy," Finance and Economics Discussion Series 2001-54, Board of Governors of the Federal Reserve System (U.S.).
  7. Canova, Fabio & Sala, Luca, 2009. "Back to square one: identification issues in DSGE models," CEPR Discussion Papers 7234, C.E.P.R. Discussion Papers.
  8. Boivin, J. & Giannoni, M., 2007. "DSGE Models in a Data-Rich Environment," Working papers 162, Banque de France.
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  10. Andrés González & Lavan Mahadeva & Juan D. Prada & Diego Rodríguez, 2011. "Policy Analysis Tool Applied to Colombian Needs: PATACON Model Description," BORRADORES DE ECONOMIA 008698, BANCO DE LA REPÚBLICA.
  11. Swanson, Eric T., 2004. "Signal Extraction And Non-Certainty-Equivalence In Optimal Monetary Policy Rules," Macroeconomic Dynamics, Cambridge University Press, vol. 8(01), pages 27-50, February.
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  16. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
  17. Pearlman, Joseph, 1986. "Diverse information and rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 10(1-2), pages 333-338, June.
  18. Michel Juillard & Douglas Laxton, 1996. "A Robust and Efficient Method for Solving Nonlinear Rational Expectations Models," IMF Working Papers 96/106, International Monetary Fund.
  19. Martin Lettau & Sydney Ludvigson, 2003. "Understanding Trend and Cycle in Asset Values: Reevaluating the Wealth Effect on Consumption," NBER Working Papers 9848, National Bureau of Economic Research, Inc.
  20. Athanasios Orphanides, 1998. "Monetary policy rules based on real-time data," Finance and Economics Discussion Series 1998-03, Board of Governors of the Federal Reserve System (U.S.).
  21. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
  22. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
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