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A Forecasting Metric for Evaluating DSGE Models for Policy Analysis

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  • Gupta, Abhishek

Abstract

This paper evaluates the strengths and weaknesses of dynamic stochastic general equilibrium (DSGE) models from the standpoint of their usefulness in doing monetary policy analysis. The paper isolates features most relevant for monetary policymaking and uses the diagnostic tools of posterior predictive analysis to evaluate these features. The paper provides a diagnosis of the observed flaws in the model with regards to these features that helps in identifying the structural flaws in the model. The paper finds that model misspecification causes certain pairs of structural shocks in the model to be correlated in order to fit the observed data.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 26718.

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Date of creation: 30 Oct 2010
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Handle: RePEc:pra:mprapa:26718

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Keywords: Posterior predictive analysis; DSGE; Monetary Policy; Forecast Errors; Model Evaluation.;

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References

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  1. Finn E. Kydland & Edward C. Prescott, 1996. "The Computational Experiment: An Econometric Tool," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 69-85, Winter.
  2. Smets, Frank & Wouters, Rafael, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 6112, C.E.P.R. Discussion Papers.
  3. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: an application to the euro area," Working Paper Research 60, National Bank of Belgium.
  4. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
  5. Edge, Rochelle M. & Kiley, Michael T. & Laforte, Jean-Philippe, 2008. "Natural rate measures in an estimated DSGE model of the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2512-2535, August.
  6. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  7. Jon Faust & Abhishek Gupta, 2012. "Posterior Predictive Analysis for Evaluating DSGE Models," NBER Working Papers 17906, National Bureau of Economic Research, Inc.
  8. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(01), pages 60-68, February.
  9. John Geweke, 2007. "Bayesian Model Comparison and Validation," American Economic Review, American Economic Association, vol. 97(2), pages 60-64, May.
  10. Frank Smets & Raf Wouters, 2002. "An estimated dynamic stochastic general equilibrium model of the euro area," Working Paper Research 35, National Bank of Belgium.
  11. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. "On the Fit of New Keynesian Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 123-143, April.
  12. 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.
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Cited by:
  1. John B. Taylor & Volker Wieland, 2012. "Surprising Comparative Properties of Monetary Models: Results from a New Model Database," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 800-816, August.
  2. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
  3. Jon Faust, 2012. "DSGE Models: I Smell a Rat (and It Smells Good)," International Journal of Central Banking, International Journal of Central Banking, vol. 8(1), pages 53-64, March.

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