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Confronting model misspecification in macroeconomics

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  • Waggoner, Daniel F.
  • Zha, Tao

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 171 (2012)
Issue (Month): 2 ()
Pages: 167-184

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Handle: RePEc:eee:econom:v:171:y:2012:i:2:p:167-184

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Markov-switching mixture; Heterogenous models; Regime-dependent weights; Model uncertainty; Parameter uncertainty; Impulse responses; Policy analysis;

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Confronting Model Misspecification in Macroeconomics
    by Christian Zimmermann in NEP-DGE blog on 2011-01-06 00:58:27
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. N. Fawcett & G. Kapetanios & J. Mitchell & S. Price, 2014. "Generalised Density Forecast Combinations," CAMA Working Papers 2014-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Robert Tetlow & Kirstin Hubrich, 2013. "Financial stress and economic dynamics: The transmission of crises," 2013 Meeting Papers 571, Society for Economic Dynamics.
  3. Wolters, Maik Hendrik, 2012. "Evaluating point and density forecasts of DSGE models," IMFS Working Paper Series 59, Institute for Monetary and Financial Stability (IMFS), Goethe University Frankfurt.
  4. Amisano, Gianni & Geweke, John, 2013. "Prediction using several macroeconomic models," Working Paper Series 1537, European Central Bank.
  5. Marco Del Negro & Frank Schorfheide, 2012. "DSGE model-based forecasting," Staff Reports 554, Federal Reserve Bank of New York.
  6. Wolters, Maik H., 2011. "Forecasting under Model Uncertainty," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48723, Verein für Socialpolitik / German Economic Association.
  7. 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.

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