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Bayesian Estimation of DSGE Models

Author

Listed:
  • Edward P. Herbst

    (Federal Reserve Board
    Division of Research and Statistics)

  • Frank Schorfheide

    (University of Pennsylvania
    National Bureau of Economic Research)

Abstract

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Suggested Citation

  • Edward P. Herbst & Frank Schorfheide, 2016. "Bayesian Estimation of DSGE Models," Economics Books, Princeton University Press, edition 1, number 10612.
  • Handle: RePEc:pup:pbooks:10612
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    Citations

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    Cited by:

    1. repec:wly:econjl:v:128:y:2018:i:611:p:1730-1757 is not listed on IDEAS
    2. Atkinson, Tyler & Richter, Alexander W. & Throckmorton, Nathaniel, 2018. "The Accuracy of Linear and Nonlinear Estimation in the Presence of the Zero Lower Bound," Working Papers 1804, Federal Reserve Bank of Dallas.
    3. Richter, Alexander W. & Throckmorton, Nathaniel, 2017. "A New Way to Quantify the Effect of Uncertainty," Working Papers 1705, Federal Reserve Bank of Dallas, revised 23 Feb 2018.
    4. Stephen McKnight & Alexander Mihailov & Antonio Pompa Rangel, 2016. "What do Latin American inflation targeters care about? A comparative Bayesian estimation of central bank preferences," Serie documentos de trabajo del Centro de Estudios Económicos 2016-08, El Colegio de México, Centro de Estudios Económicos.
    5. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    6. Richter, Alexander W. & Throckmorton, Nathaniel, 2016. "Are nonlinear methods necessary at the zero lower bound?," Working Papers 1606, Federal Reserve Bank of Dallas.
    7. Maik H. Wolters, 2018. "How the baby boomers' retirement wave distorts model‐based output gap estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 680-689, August.
    8. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    9. Doh, Taeyoung & Wu, Shu, 2015. "Cash flow and risk premium dynamics in an equilibrium asset-pricing model with recursive preferences," Research Working Paper RWP 15-12, Federal Reserve Bank of Kansas City.
    10. Richter, Alexander W. & Throckmorton, Nathaniel A., 2016. "Is Rotemberg pricing justified by macro data?," Economics Letters, Elsevier, vol. 149(C), pages 44-48.
    11. Michael Plante & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Endogenous Uncertainty," Economic Journal, Royal Economic Society, vol. 128(611), pages 1730-1757, June.

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