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Frequentist Inference in Weakly Identified DSGE Models

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  • Guerron-Quintana, Pablo A.
  • Inoue, Atsushi
  • Kilian, Lutz

Abstract

We 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, we develop new frequentist confidence sets for structural DSGE model parameters that remain asymptotically valid regardless of the strength of the identification.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7447.

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Date of creation: Sep 2009
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Handle: RePEc:cpr:ceprdp:7447

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Related research

Keywords: Bayes factor; Bayesian estimation; Confidence set; DSGE models; Identification; Inference; Likelihood ratio;

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References

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  1. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2005. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," NBER Working Papers 11034, National Bureau of Economic Research, Inc.
  2. 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.
  3. 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.
  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. 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.
  6. 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.
  7. Chaudhuri, Saraswata & Zivot, Eric, 2011. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Journal of Econometrics, Elsevier, vol. 164(2), pages 239-251, October.
  8. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
  9. 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.
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Cited by:
  1. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
  2. Òscar Jordà & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Economics Working Papers ECO2008/34, European University Institute.
  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. 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.

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