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Bayesian Estimation of DSGE Models: Is the Workhorse Model Identified?


  • Evren Caglar

    () (University of Kent)

  • Jagjit S. Chadha

    () (University of Kent)

  • Katsuyuki Shibayama

    () (University of Kent)


Koop, Pesaran and Smith (2011) suggest a simple diagnostic indicator for the Bayesian estimation of the parameters of a DSGE model. They show that, if a parameter is well identified, the precision of the posterior should improve as the (artificial) data size T increases, and the indicator checks the speed at which precision improves. It does not require any additional programming; a researcher just needs to generate artificial data and estimate the model with different T. Applying this to Smets and Wouters'(2007) medium size US model, we find that while exogenous shock processes are well identified, most of the parameters in the structural equations are not.

Suggested Citation

  • Evren Caglar & Jagjit S. Chadha & Katsuyuki Shibayama, 2012. "Bayesian Estimation of DSGE Models: Is the Workhorse Model Identified?," Koç University-TUSIAD Economic Research Forum Working Papers 1205, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1205

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    References listed on IDEAS

    1. Nikolay Iskrev, 2010. "Parameter identification in Dynamic Economic models," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    2. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    3. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
    4. Consolo, Agostino & Favero, Carlo A. & Paccagnini, Alessia, 2009. "On the statistical identification of DSGE models," Journal of Econometrics, Elsevier, vol. 150(1), pages 99-115, May.
    5. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
    6. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
    7. Marco Ratto & Werner Roeger, 2005. "An estimated open-economy model for the EURO area," Computing in Economics and Finance 2005 84, Society for Computational Economics.
    8. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
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    Cited by:

    1. Chatelain, Jean-Bernard & Ralf, Kirsten, 2014. "Stability and Identification with Optimal Macroprudential Policy Rules," EconStor Preprints 95979, ZBW - German National Library of Economics.
    2. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.
    3. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2017. "How optimal is US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 92(C), pages 96-111.
    4. Thomai Filippeli & Konstantinos Theodoridis, 2015. "DSGE priors for BVAR models," Empirical Economics, Springer, vol. 48(2), pages 627-656, March.

    More about this item


    Bayesian Estimation; Dynamic stochastic general equilibrium models; Identification.;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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