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Confronting Model Misspecification in Macroeconomics

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  • Daniel F. Waggoner
  • Tao Zha

Abstract

We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized DSGE model and a corresponding BVAR model. We show that the Markov-switching mixture model dominates both individual models and improves the fit considerably. Our estimation indicates that the DSGE model plays an important role only in the late 1970s and the early 1980s. We show how to use the mixture model as a data filter for estimation of the DSGE model when the BVAR model is not identified. Moreover, we show how to compute the impulse responses to the same type of shock shared by the DSGE and BVAR models when the shock is identified in the BVAR model. Our exercises demonstrate the importance of integrating model uncertainty and parameter uncertainty to address potential model misspecification in macroeconomics.

Suggested Citation

  • Daniel F. Waggoner & Tao Zha, 2012. "Confronting Model Misspecification in Macroeconomics," NBER Working Papers 17791, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17791
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    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    2. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    3. Maik H. Wolters, 2015. "Evaluating Point and Density Forecasts of DSGE Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 74-96, January.
    4. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    5. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
    6. Thorsten Drautzburg, 2014. "A Narrative Approach to a Fiscal DSGE Model," 2014 Meeting Papers 791, Society for Economic Dynamics.
    7. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    8. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    9. Morrisy, Stephen D., 2017. "Efficient estimation of macroeconomic equations with unobservable states," Economic Modelling, Elsevier, vol. 60(C), pages 408-423.
    10. repec:eee:macchp:v2-527 is not listed on IDEAS
    11. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España;Working Papers Homepage.
    12. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    13. Hubrich, Kirstin & Tetlow, Robert J., 2015. "Financial stress and economic dynamics: The transmission of crises," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 100-115.
    14. Mester, Loretta J., 2016. "Acknowledging Uncertainty, 10-07-2016; Shadow Open Market Committee Fall Meeting, New York, NY," Speech 77, Federal Reserve Bank of Cleveland.
    15. Kapetanios, G. & Mitchell, J. & Price, S. & Fawcett, N., 2015. "Generalised density forecast combinations," Journal of Econometrics, Elsevier, vol. 188(1), pages 150-165.
    16. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters,in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
    17. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2018. "Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods," MPRA Paper 85523, University Library of Munich, Germany.
    18. Chung, Tsz-Kin & Iiboshi, Hirokuni, 2015. "Prediction of Term Structure with Potentially Misspecified Macro-Finance Models near the Zero Lower Bound," MPRA Paper 85709, University Library of Munich, Germany.
    19. repec:bla:obuest:v:79:y:2017:i:4:p:495-512 is not listed on IDEAS
    20. Amisano, Gianni & Geweke, John, 2013. "Prediction using several macroeconomic models," Working Paper Series 1537, European Central Bank.
    21. repec:taf:defpea:v:28:y:2017:i:5:p:585-608 is not listed on IDEAS
    22. Iiboshi, Hirokuni, 2016. "A multiple DSGE-VAR approach: Priors from a combination of DSGE models and evidence from Japan," Japan and the World Economy, Elsevier, vol. 40(C), pages 1-8.
    23. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates

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