IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/1131.html
   My bibliography  Save this paper

On Identification of Bayesian DSGE Models

Author

Abstract

In recent years there has been increasing concern about the identification of parameters in dynamic stochastic general equilibrium (DSGE) models. Given the structure of DSGE models it may be difficult to determine whether a parameter is identified. For the researcher using Bayesian methods, a lack of identification may not be evident since the posterior of a parameter of interest may differ from its prior even if the parameter is unidentified. We show that this can even be the case even if the priors assumed on the structural parameters are independent. We suggest two Bayesian identification indicators that do not suffer from this difficulty and are relatively easy to compute. The first applies to DSGE models where the parameters can be partitioned into those that are known to be identified and the rest where it is not known whether they are identified. In such cases the marginal posterior of an unidentified parameter will equal the posterior expectation of the prior for that parameter conditional on the identified parameters. The second indicator is more generally applicable and considers the rate at which the posterior precision gets updated as the sample size ( T ) is increased. For identified parameters the posterior precision rises with T , whilst for an unidentified parameter its posterior precision may be updated but its rate of update will be slower than T . This result assumes that the identified parameters are -consistent, but similar differential rates of updates for identified and unidentified parameters can be established in the case of super consistent estimators. These results are illustrated by means of simple DSGE models.

Suggested Citation

  • Koop, G. & Pesaran, M.H. & Smith, R., 2011. "On Identification of Bayesian DSGE Models," Cambridge Working Papers in Economics 1131, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1131
    as

    Download full text from publisher

    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1131.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
    2. Hoogerheide, Lennart & Kleibergen, Frank & van Dijk, Herman K., 2007. "Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data," Journal of Econometrics, Elsevier, vol. 138(1), pages 63-103, May.
    3. Pesaran, M. H., 1981. "Identification of rational expectations models," Journal of Econometrics, Elsevier, vol. 16(3), pages 375-398, August.
    4. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    5. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    6. Kleibergen, Frank & Zivot, Eric, 2003. "Bayesian and classical approaches to instrumental variable regression," Journal of Econometrics, Elsevier, vol. 114(1), pages 29-72, May.
    7. Dreze, Jacques H. & Richard, Jean-Francois, 1983. "Bayesian analysis of simultaneous equation systems," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 9, pages 517-598, Elsevier.
    8. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    9. Stephane Dees & M. Hashem Pesaran & L. Vanessa Smith & Ron P. Smith, 2009. "Identification of New Keynesian Phillips Curves from a Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1481-1502, October.
    10. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    11. Pablo A. Guerron-Quintana, 2010. "What you match does matter: the effects of data on DSGE estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 774-804.
    12. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    13. Koop, Gary & Poirier, Dale J., 1997. "Learning about the across-regime correlation in switching regression models," Journal of Econometrics, Elsevier, vol. 78(2), pages 217-227, June.
    14. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    15. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    16. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    17. Evren Caglar & Jagjit S. Chadha & Katsuyuki Shibayama, 2011. "Bayesian Estimation of DSGE models: Is the Workhorse Model Identified?," Studies in Economics 1125, School of Economics, University of Kent.
    18. Lars Peter Hansen & James J. Heckman, 1996. "The Empirical Foundations of Calibration," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 87-104, Winter.
    19. Gali, Jordi & Gertler, Mark & David Lopez-Salido, J., 2005. "Robustness of the estimates of the hybrid New Keynesian Phillips curve," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1107-1118, September.
    20. Kleibergen, Frank & van Dijk, Herman K., 1998. "Bayesian Simultaneous Equations Analysis Using Reduced Rank Structures," Econometric Theory, Cambridge University Press, vol. 14(6), pages 701-743, December.
    21. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    22. Thomas A. Lubik & Frank Schorfheide, 2004. "Testing for Indeterminacy: An Application to U.S. Monetary Policy," American Economic Review, American Economic Association, vol. 94(1), pages 190-217, March.
    23. Finn E. Kydland & Edward C. Prescott, 1996. "The Computational Experiment: An Econometric Tool," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 69-85, Winter.
    24. Beyer, Andreas & Farmer, Roger E. A., 2004. "On the indeterminacy of new-Keynesian economics," Working Paper Series 323, European Central Bank.
    25. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    26. Benati, Luca, 2010. "Are policy counterfactuals based on structural VAR's reliable?," Working Paper Series 1188, European Central Bank.
    27. Wallis, Kenneth F, 1980. "Econometric Implications of the Rational Expectations Hypothesis," Econometrica, Econometric Society, vol. 48(1), pages 49-73, January.
    28. James M. Nason & Gregor W. Smith, 2008. "Identifying the new Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 525-551.
    29. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2008. "Factor analysis in a model with rational expectations," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 271-286, July.
    30. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.
    31. Pudney, S. E., 1982. "The identification of rational expectations models under structural neutrality," Journal of Economic Dynamics and Control, Elsevier, vol. 4(1), pages 117-121, November.
    32. Binder, Michael & Pesaran, M. Hashem, 1997. "Multivariate Linear Rational Expectations Models," Econometric Theory, Cambridge University Press, vol. 13(6), pages 877-888, December.
    33. Fabio Canova, 2007. "Bayesian Analysis of DSGE Models by S. An and F. Schorfheide," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 187-192.
    34. Kleibergen, Frank & Mavroeidis, Sophocles, 2009. "Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 293-311.
    35. Sargent, Thomas J, 1976. "The Observational Equivalence of Natural and Unnatural Rate Theories of Macroeconomics," Journal of Political Economy, University of Chicago Press, vol. 84(3), pages 631-640, June.
    36. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    37. Mavroeidis, Sophocles, 2005. "Identification Issues in Forward-Looking Models Estimated by GMM, with an Application to the Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 421-448, June.
    38. Binder,M. & Pesaran,H.M., 1995. "Multivariate Rational Expectations Models and Macroeconomic Modelling: A Review and Some New Results," Cambridge Working Papers in Economics 9415, Faculty of Economics, University of Cambridge.
    39. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    40. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    41. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    42. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October.
    43. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    44. McCallum, Bennett T, 1979. "On the Observational Inequivalence of Classical and Keynesian Models," Journal of Political Economy, University of Chicago Press, vol. 87(2), pages 395-402, April.
    45. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
    46. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Taremi, Mohammad & Esksndari, Farzad & Bameni Moghadam, Mohammad, 2016. "Identifiability of Dynamic Stochastic General Equilibrium Models with Covariance Restrictions," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 11(3), pages 225-243, July.
    2. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    3. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    4. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2010. "RBCs AND DSGEs: THE COMPUTATIONAL APPROACH TO BUSINESS CYCLE THEORY AND EVIDENCE," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 113-136, February.
    5. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    6. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    7. Luca Fanelli, 2009. "Estimation of quasi-rational DSGE monetary models," Quaderni di Dipartimento 3, Department of Statistics, University of Bologna.
    8. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
    9. Stephane Dees & M. Hashem Pesaran & L. Vanessa Smith & Ron P. Smith, 2009. "Identification of New Keynesian Phillips Curves from a Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1481-1502, October.
    10. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    11. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    12. Ríos-Rull, José-Víctor & Schorfheide, Frank & Fuentes-Albero, Cristina & Kryshko, Maxym & Santaeulàlia-Llopis, Raül, 2012. "Methods versus substance: Measuring the effects of technology shocks," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 826-846.
    13. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    14. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    15. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.
    16. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1439-1512, Elsevier.
    17. Milani, Fabio, 2007. "Expectations, learning and macroeconomic persistence," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2065-2082, October.
    18. Bertille Antoine & Otilia Boldea, 2015. "Efficient Inference with Time-Varying Information and the New Keynesian Phillips Curve," Discussion Papers dp15-04, Department of Economics, Simon Fraser University, revised 25 Aug 2016.
    19. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-31.
    20. Carlo A. Favero, 2009. "The Econometrics of Monetary Policy: An Overview," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 16, pages 821-850, Palgrave Macmillan.

    More about this item

    Keywords

    Bayesian identification; DSGE models; posterior updating; New Keynesian Phillips Curve.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cam:camdae:1131. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.