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Frequentist inference in weakly identified DSGE models

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  • Pablo Guerron-Quintana
  • Atsushi Inoue
  • Lutz Kilian

Abstract

The authors show that in weakly identified models (1) the posterior mode will not be a consistent estimator of the true parameter vector, (2) the posterior distribution will not be Gaussian even asymptotically, and (3) Bayesian credible sets and frequentist confidence sets will not coincide asymptotically. This means that Bayesian DSGE estimation should not be interpreted merely as a convenient device for obtaining asymptotically valid point estimates and confidence sets from the posterior distribution. As an alternative, the authors develop a new class of frequentist confidence sets for structural DSGE model parameters that remains asymptotically valid regardless of the strength of the identification. The proposed set correctly reflects the uncertainty about the structural parameters even when the likelihood is flat, it protects the researcher from spurious inference, and it is asymptotically invariant to the prior in the case of weak identification.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 09-13.

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Date of creation: 2009
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Handle: RePEc:fip:fedpwp:09-13

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Keywords: Stochastic analysis ; Macroeconomics - Econometric models;

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References

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  1. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
  2. Peter J. Klenow & Oleksiy Kryvtsov, 2005. "State-Dependent or Time-Dependent Pricing: Does It Matter for Recent U.S. Inflation?," Working Papers 05-4, Bank of Canada.
  3. Saraswata Chaudhuri & Eric Zivot, 2008. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Working Papers UWEC-2008-26, University of Washington, Department of Economics.
  4. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2005. "Online Appendix to "Firm-Specific Capital, Nominal Rigidities and the Business Cycle"," Technical Appendices 09-191, Review of Economic Dynamics.
  5. David Altig & Lawrence J. Christiano & Martin Eichenbaum & Jesper Linde, 2004. "Firm-specific capital, nominal rigidities and the business cycle," Working Paper Series WP-05-01, Federal Reserve Bank of Chicago.
  6. Charles Nelson & Richard Startz, 2007. "The Zero-Information-Limit-Condition and Spurious Inference in Weakly Identified Models," Working Papers UWEC-2006-07-P, University of Washington, Department of Economics.
  7. Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," Working Paper 2008-18, Federal Reserve Bank of Atlanta.
  8. Choi, In & Phillips, Peter C. B., 1992. "Asymptotic and finite sample distribution theory for IV estimators and tests in partially identified structural equations," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 113-150.
  9. Juan Francisco Rubio-Ramírez & Daniel Waggoner & Tao Zha, 2005. "Markov-switching structural vector autoregressions: theory and application," Working Paper 2005-27, Federal Reserve Bank of Atlanta.
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Cited by:
  1. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," Working Papers w201032, Banco de Portugal, Economics and Research Department.
  2. �scar Jord� & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
  3. Frank Schorfheide, 2011. "Estimation and Evaluation of DSGE Models: Progress and Challenges," NBER Working Papers 16781, National Bureau of Economic Research, Inc.
  4. 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.
  5. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.

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