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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. Coenen, Günter & Levin, Andrew T. & Wieland, Volker, 2001. "Data uncertainty and the role of money as an information variable for monetary policy," Working Paper Series 0084, European Central Bank.
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  9. Uhlig, H.F.H.V.S., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
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  12. Frank Schorfheide & Keith Sill & Maxym Kryshko, 2008. "DSGE model-based forecasting of non-modelled variables," Working Papers 08-17, Federal Reserve Bank of Philadelphia.
  13. Svensson, Lars E. O. & Woodford, Michael, 2003. "Indicator variables for optimal policy," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 691-720, April.
  14. Michel Juillard & Douglas Laxton, 1996. "A Robust and Efficient Method for Solving Nonlinear Rational Expectations Models," IMF Working Papers 96/106, International Monetary Fund.
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  16. 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.).
  17. Tinsley, P. A. & Spindt, P. A. & Friar, M. E., 1980. "Indicator and filter attributes of monetary aggregates : A nit-picking case for disaggregation," Journal of Econometrics, Elsevier, vol. 14(1), pages 61-91, September.
  18. Andrés González & Lavan Mahadeva & Juan D. Prada & Diego Rodríguez, 2011. "Policy Analysis Tool Applied to Colombian Needs: Patacon Model Description," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  19. Gerali, Andrea & Lippi, Francesco, 2003. "Optimal Control and Filtering in Linear Forward-looking Economies: A Toolkit," CEPR Discussion Papers 3706, C.E.P.R. Discussion Papers.
  20. Adolfson, Malin & Andersson, Michael K. & Lindé, Jesper & Villani, Mattias & Vredin, Anders, 2005. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," Working Paper Series 188, Sveriges Riksbank (Central Bank of Sweden), revised 01 Jun 2006.
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  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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