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Posterior Predictive Analysis for Evaluating DSGE Models

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  • Jon Faust
  • Abhishek Gupta

Abstract

While dynamic stochastic general equilibrium (DSGE) models for monetary policy analysis have come a long way, there is considerable difference of opinion over the role these models should play in the policy process. The paper develops three main points about assessing the value of these models. First, we document that DSGE models continue to have aspects of crude approximation and omission. This motivates the need for tools to reveal the strengths and weaknesses of the models--both to direct development efforts and to inform how best to use the current flawed models. Second, posterior predictive analysis provides a useful and economical tool for finding and communicating strengths and weaknesses. In particular, we adapt a form of discrepancy analysis as proposed by Gelman, et al. (1996). Third, we provide a nonstandard defense of posterior predictive analysis in the DSGE context against long-standing objections. We use the iconic Smets-Wouters model for illustrative purposes, showing a number of heretofore unrecognized properties that may be important from a policymaking perspective.

Suggested Citation

  • Jon Faust & Abhishek Gupta, 2012. "Posterior Predictive Analysis for Evaluating DSGE Models," NBER Working Papers 17906, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17906
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    1. Gupta, Abhishek, 2010. "A Forecasting Metric for Evaluating DSGE Models for Policy Analysis," MPRA Paper 26718, University Library of Munich, Germany.
    2. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    3. Christopher D. Carroll & Jiri Slacalek & Martin Sommer, 2011. "International Evidence on Sticky Consumption Growth," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1135-1145, November.
    4. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    5. Orazio P. Attanasio & Guglielmo Weber, 1993. "Consumption Growth, the Interest Rate and Aggregation," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 631-649.
    6. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, October.
    7. 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.
    8. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters,in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
    9. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    10. 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.
    11. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    12. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters,in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
    13. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    14. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Business Cycle Accounting," Econometrica, Econometric Society, vol. 75(3), pages 781-836, May.
    15. Karen E. Dynan, 2000. "Habit Formation in Consumer Preferences: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 90(3), pages 391-406, June.
    16. Mehra, Rajnish & Prescott, Edward C., 1985. "The equity premium: A puzzle," Journal of Monetary Economics, Elsevier, vol. 15(2), pages 145-161, March.
    17. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
    18. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(01), pages 60-68, February.
    19. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    20. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    21. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    22. Jon Faust & Jonathan H. Wright, 2007. "Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset," NBER Working Papers 13397, National Bureau of Economic Research, Inc.
    23. Wilcox, David W, 1992. "The Construction of U.S. Consumption Data: Some Facts and Their Implications for Empirical Work," American Economic Review, American Economic Association, vol. 82(4), pages 922-941, September.
    24. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    25. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
    26. John Geweke, 2007. "Bayesian Model Comparison and Validation," American Economic Review, American Economic Association, vol. 97(2), pages 60-64, May.
    27. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    28. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Cited by:

    1. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    2. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
    3. Michal Andrle & Jan Bruha & Serhat Solmaz, 2016. "On the Sources of Business Cycles: Implications for DSGE Models," Working Papers 2016/03, Czech National Bank, Research Department.
    4. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    5. Gupta, Abhishek, 2010. "A Forecasting Metric for Evaluating DSGE Models for Policy Analysis," MPRA Paper 26718, University Library of Munich, Germany.
    6. Eric M. Leeper & Nora Traum & Todd B. Walker, 2015. "Clearing Up the Fiscal Multiplier Morass: Prior and Posterior Analysis," NBER Working Papers 21433, National Bureau of Economic Research, Inc.
    7. Fabio Canova & Filippo Ferroni & Christian Matthes, 2014. "Choosing The Variables To Estimate Singular Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1099-1117, November.
    8. Eric M. Leeper & Nora Traum & Todd B. Walker, 2017. "Clearing Up the Fiscal Multiplier Morass," American Economic Review, American Economic Association, vol. 107(8), pages 2409-2454, August.
    9. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. 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.
    11. Abhishek Gupta, 2016. "A Forecasting Metric for Evaluating DSGE Models for Policy Analysis," International Journal of Central Banking, International Journal of Central Banking, vol. 12(1), pages 33-65, March.
    12. Richter, Alexander W. & Throckmorton, Nathaniel, 2016. "Are nonlinear methods necessary at the zero lower bound?," Working Papers 1606, Federal Reserve Bank of Dallas.
    13. Malte Rieth, 2017. "Capital Taxation and Government Debt Policy with Public Discounting," Discussion Papers of DIW Berlin 1697, DIW Berlin, German Institute for Economic Research.
    14. Michal Andrle & Jaromir Benes, 2013. "System Priors; Formulating Priors about DSGE Models' Properties," IMF Working Papers 13/257, International Monetary Fund.
    15. repec:eee:dyncon:v:85:y:2017:i:c:p:1-20 is not listed on IDEAS

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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