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Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future

Author Info

Listed author(s):
  • Andrés González Gómez

    ()

  • Lavan Mahadeva

    ()

  • Diego Rodríguez

    ()

  • Luis Eduardo Rojas

    ()

Abstract

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

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Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 559.

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Date of creation: Apr 2009
Handle: RePEc:bdr:borrec:559
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Keywords: Monetary Policy; DSGE; Forecast; Kalman Filter Classification JEL: F47; E01; C61.;

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Find related papers by JEL classification:
  • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
  • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
  • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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  1. S. Boragan Aruoba, 2004. "Data Uncertainty in General Equilibrium," Computing in Economics and Finance 2004 131, Society for Computational Economics.
  2. 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.
  3. Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010. "DSGE model-based forecasting of non-modelled variables," International Journal of Forecasting, Elsevier, vol. 26(2), pages 348-373, April.
  4. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
  5. 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, vol. 29(66), pages 222-245, December.
  6. Svensson, Lars E. O. & Woodford, Michael, 2003. "Indicator variables for optimal policy," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 691-720, April.
  7. Coenen, Gunter & Levin, Andrew & Wieland, Volker, 2005. "Data uncertainty and the role of money as an information variable for monetary policy," European Economic Review, Elsevier, vol. 49(4), pages 975-1006, May.
  8. Martin Fukac & Adrian Pagan, 2006. "Issues in Adopting DSGE Models for Use in the Policy Process," Working Papers 2006/6, Czech National Bank, Research Department.
  9. 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.
  10. Martin Lettau & Sydney C. Ludvigson, 2004. "Understanding Trend and Cycle in Asset Values: Reevaluating the Wealth Effect on Consumption," American Economic Review, American Economic Association, vol. 94(1), pages 276-299, March.
  11. Harald Uhlig, 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics 101, Federal Reserve Bank of Minneapolis.
  12. 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.
  13. 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.
  14. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
  15. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
  16. Malin Adolfson & Michael K. Andersson & Jesper Lindé & Mattias Villani & Anders Vredin, 2007. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 111-144, December.
  17. 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.
  18. Svensson, Lars E. O. & Woodford, Michael, 2004. "Indicator variables for optimal policy under asymmetric information," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 661-690, January.
  19. Canova, Fabio & Sala, Luca, 2006. "Back to square one: identification issues in DSGE models," Working Paper Series 583, European Central Bank.
  20. P.A. Tinsley & P.A. Spindt & M.E. Friar, 1980. "Indicator and filter attributes of monetary aggregates: a nit-picking case for disaggregation," Special Studies Papers 140, Board of Governors of the Federal Reserve System (U.S.).
  21. 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.
  22. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
  23. Pearlman, Joseph, 1986. "Diverse information and rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 10(1-2), pages 333-338, June.
  24. 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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Cited by:

  1. 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, vol. 29(66), pages 222-245, December.
  2. Luis E. Rojas, 2011. "Professional Forecasters: How to Understand and Exploit Them Through a DSGE Model," Borradores de Economia 664, Banco de la Republica de Colombia.
  3. Ramiro Rodríguez Revilla, 2011. "Modelos de equilibrio general dinámicos y estocásticos para Colombia 1995-2011," REVISTA ECOS DE ECONOMÍA, UNIVERSIDAD EAFIT, December.

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