IDEAS home Printed from https://ideas.repec.org/a/prs/ecoprv/ecop_0249-4744_2008_num_183_2_7810.html
   My bibliography  Save this article

Un regard bayésien sur les modèles dynamiques de la macroéconomie

Author

Listed:
  • Florian Pelgrin
  • Stéphane Adjemian

Abstract

[eng] Our article describes the Bayesian approach to the most highly regarded dynamic models in macroeconomics : DSGE (dynamic stochastic general-equilibrium) models and VAR (vector autoregressive) models. We present the main concepts in Bayesian analysis and show how to apply them to VAR models. We then explore the specific features of the Bayesian approach to DSGE models. Unlike VAR models, DSGE models cannot provide an analytical expression of the posterioi distribution. To overcome this difficulty we must resort to Monte-Carlo methods, whose main features we describe. Lastly , to underscore how sterile the VAR / DSGE opposition is, we describe a recent approach that combines the best aspects of both models. [fre] L’objet de cet article est de présenter l’approche bayésienne des modèles dynamiques les plus considérés en macroéconomie : les modèles DSGE (Dynamic Stochastic General Equilibrium ) et les modèles VAR. Nous présentons les principaux concepts de l’analyse bayésienne et montrons comment les appliquer dans le cadre des modèles VAR. Nous abordons ensuite les spécificités de l’approche bayésienne des modèles DSGE. Contrairement aux modèles VAR, il n’est plus possible d’obtenir une expression analytique de la distribution a posteriori. Pour pallier cette difficulté il est nécessaire de recourir à des méthodes de Monte-Carlo dont nous décrivons les principales techniques. Enfin, afin de souligner la nature stérile de l'opposition entre ces deux types de modélisation, nous terminons en présentant une approche récente permettant de combiner le meilleur des approches VAR et DSGE.

Suggested Citation

  • Florian Pelgrin & Stéphane Adjemian, 2008. "Un regard bayésien sur les modèles dynamiques de la macroéconomie," Économie et Prévision, Programme National Persée, vol. 183(2), pages 127-152.
  • Handle: RePEc:prs:ecoprv:ecop_0249-4744_2008_num_183_2_7810
    Note: DOI:10.3406/ecop.2008.7810
    as

    Download full text from publisher

    File URL: https://doi.org/10.3406/ecop.2008.7810
    Download Restriction: no

    File URL: https://www.persee.fr/doc/ecop_0249-4744_2008_num_183_2_7810
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Canova, Fabio, 1994. "Statistical Inference in Calibrated Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(S), pages 123-144, Suppl. De.
    2. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
    3. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    4. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, March.
    5. Smets, Frank & Wouters, Raf, 2002. "An estimated stochastic dynamic general equilibrium model of the euro area," Working Paper Series 0171, European Central Bank.
    6. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    7. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    8. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    9. Sims, Christopher A & Uhlig, Harald, 1991. "Understanding Unit Rooters: A Helicopter Tour," Econometrica, Econometric Society, vol. 59(6), pages 1591-1599, November.
    10. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, March.
    11. Dridi, Ramdan & Guay, Alain & Renault, Eric, 2007. "Indirect inference and calibration of dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 136(2), pages 397-430, February.
    12. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    13. Bierens, Herman J., 2007. "Econometric analysis of linearized singular dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 136(2), pages 595-627, February.
    14. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    15. Campbell, John Y., 1994. "Inspecting the mechanism: An analytical approach to the stochastic growth model," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 463-506, June.
    16. Bernanke, Ben S., 1986. "Alternative explanations of the money-income correlation," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 49-99, January.
    17. Phillips, P C B, 1991. "Bayesian Routes and Unit Roots: De Rebus Prioribus Semper Est Disputandum," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 435-473, Oct.-Dec..
    18. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
    19. Phillips, P C B, 1991. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 333-364, Oct.-Dec..
    20. Phillips, Peter C B, 1996. "Econometric Model Determination," Econometrica, Econometric Society, vol. 64(4), pages 763-812, July.
    21. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    22. Christopher A. Sims, 1991. "Comment on 'To Criticize the Critics,' by Peter C. B. Phillips," Cowles Foundation Discussion Papers 985, Cowles Foundation for Research in Economics, Yale University.
    23. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
    24. Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters,in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
    25. Rabanal, Pau & Rubio-Ramirez, Juan F., 2005. "Comparing New Keynesian models of the business cycle: A Bayesian approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1151-1166, September.
    26. Frank Smets & Raf Wouters, 2002. "Monetary policy in an estimated stochastic dynamic general equilibrium model of the Euro area," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    27. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    28. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    29. Sims, Christopher A, 1991. "To Criticize the Critics: Comment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 423-434, Oct.-Dec..
    30. 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.
    31. DeJong, David N & Ingram, Beth Fisher & Whiteman, Charles H, 1996. "A Bayesian Approach to Calibration," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 1-9, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicolas Maggiar & Antoine Devulder & Christophe Cahn & Stéphane Adjemian, 2008. "Variantes en univers incertain," Économie et Prévision, Programme National Persée, vol. 183(2), pages 223-238.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prs:ecoprv:ecop_0249-4744_2008_num_183_2_7810. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Equipe PERSEE). General contact details of provider: https://www.persee.fr/collection/ecop .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.