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On Identification of Bayesian DSGE Models

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Author Info

  • Koop, Gary

    ()
    (University of Strathclyde)

  • Pesaran, M. Hashem

    ()
    (University of Cambridge)

  • Smith, Ron P.

    ()
    (Birkbeck College, University of London)

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 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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 5638.

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Length: 39 pages
Date of creation: Apr 2011
Date of revision:
Handle: RePEc:iza:izadps:dp5638

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Keywords: Bayesian identification; DSGE models; posterior updating; New Keynesian Phillips Curve;

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References

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  1. Del Negro, Marco & Schorfheide, Frank, 2007. "Forming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities)," CEPR Discussion Papers, C.E.P.R. Discussion Papers 6119, C.E.P.R. Discussion Papers.
  2. Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," Working Paper, Federal Reserve Bank of Atlanta 2008-18, Federal Reserve Bank of Atlanta.
  3. Pudney, S. E., 1982. "The identification of rational expectations models under structural neutrality," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 4(1), pages 117-121, November.
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Cited by:
  1. Martinez-Garcia, Enrique & Wynne, Mark A., 2014. "Assessing Bayesian Model Comparison in Small Samples," Globalization and Monetary Policy Institute Working Paper, Federal Reserve Bank of Dallas 189, Federal Reserve Bank of Dallas.
  2. Kai Liu, 2014. "Public Finances, Business Cycles and Structural Fiscal Balances," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 1411, Faculty of Economics, University of Cambridge.
  3. Hashem Pesaran & Ron Smith, 2014. "Tests of Policy Ineffectiveness in Macroeconometrics," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 1415, Faculty of Economics, University of Cambridge.
  4. Pesaran, M. Hashem & Smith, Ron P., 2011. "Beyond the DSGE Straitjacket," IZA Discussion Papers 5661, Institute for the Study of Labor (IZA).
  5. Alfred Stiassny & Christina Uhl, 2014. "Does Elderly Employment have an Impact on Youth Employment? A General Equilibrium Approach," Department of Economics Working Papers, Vienna University of Economics, Department of Economics wuwp178, Vienna University of Economics, Department of Economics.

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