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Exploiting the monthly data-flow in structural forecasting

Author Info

  • Domenico Giannone

    ()

    (Libera Università Internazionale degli Studi Sociali Guido Carli (LUISS)
    Centre for Economic Policy Research (CEPR))

  • Francesca Monti

    ()

    (Bank of England
    Centre for Macroeconomics (CFM))

  • Lucrezia Reichlin

    ()

    (London Business School (LBS)
    Centre for Economic Policy Research (CEPR))

Abstract

This paper shows how and when it is possible to obtain a mapping from a quarterly DSGE model to a monthly specification that maintains the same economic restrictions and has real coefficients. We use this technique to derive the monthly counterpart of the Gali et al (2011) model. We then augment it with auxiliary macro indicators which, because of their timeliness, can be used to obtain a now-cast of the structural model. We show empirical results for the quarterly growth rate of GDP, the monthly unemployment rate and the welfare relevant output gap defined in Gali, Smets and Wouters (2011). Results show that the augmented monthly model does best for now-casting.

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File URL: http://www.centreformacroeconomics.ac.uk/Discussion-Papers/2014/CFMDP2014-16-Paper.pdf
File Function: First version, 2014
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Paper provided by Centre for Macroeconomics (CFM) in its series Discussion Papers with number 1416.

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Length: 23 pages
Date of creation: Jun 2014
Date of revision:
Handle: RePEc:cfm:wpaper:1416
Contact details of provider: Web page: http://www.centreformacroeconomics.ac.uk/

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Keywords: DSGE Models; Forecasting; Temporal Aggregation; Mixed Frequency Data; Large Datasets;

Other versions of this item:

Find related papers by JEL classification:
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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  1. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2014. "Understanding the Great Recession," NBER Working Papers 20040, National Bureau of Economic Research, Inc.
  2. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
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  5. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
  6. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
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  12. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.
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  15. Červená, Marianna & Schneider, Martin, 2014. "Short-term forecasting of GDP with a DSGE model augmented by monthly indicators," International Journal of Forecasting, Elsevier, vol. 30(3), pages 498-516.
  16. Galí, Jordi & Smets, Frank & Wouters, Rafael, 2011. "Unemployment in an Estimated New Keynesian Model," CEPR Discussion Papers 8401, C.E.P.R. Discussion Papers.
  17. Bernanke, B. & Gertler, M. & Gilchrist, S., 1998. "The Financial Accelerator in a Quantitative Business Cycle Framework," Working Papers 98-03, C.V. Starr Center for Applied Economics, New York University.
  18. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
  19. Francesca Monti, 2010. "Combining Judgment and Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(8), pages 1641-1662, December.
  20. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
  21. Jing Cynthia Wu & Fan Dora Xia, 2014. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," NBER Working Papers 20117, National Bureau of Economic Research, Inc.
  22. Jean Boivin & Marc Giannoni, 2006. "DSGE Models in a Data-Rich Environment," NBER Working Papers 12772, National Bureau of Economic Research, Inc.
  23. Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
  24. Marianna Cervená & Martin Schneider, 2010. "Short-term forecasting GDP with a DSGE model augmented by monthly indicators," Working Papers 163, Oesterreichische Nationalbank (Austrian Central Bank).
  25. Christoffel, Kai & Coenen, Günter & Warne, Anders, 2008. "The New Area-Wide Model of the euro area: a micro-founded open-economy model for forecasting and policy analysis," Working Paper Series 0944, European Central Bank.
  26. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2011. "Estimating Dynamic Equilibrium Models using Macro and Financial Data," CREATES Research Papers 2011-21, Department of Economics and Business Economics, Aarhus University.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Exploiting the monthly data flow in structural forecasting
    by Christian Zimmermann in NEP-DGE blog on 2014-10-05 17:06:38
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Cited by:

  1. Krueger, Fabian & Clark, Todd E. & Ravazzolo, Francesco, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Paper 1439, Federal Reserve Bank of Cleveland.
  2. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo Group Munich.

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