On Identification of Bayesian DSGE Models
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 pT-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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- Juan F. Rubio-Ram�rez & Daniel F. Waggoner & Tao Zha, 2010.
"Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,"
Review of Economic Studies,
Oxford University Press, vol. 77(2), pages 665-696.
- Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," FRB Atlanta Working Paper No. 2008-18, Federal Reserve Bank of Atlanta.
- Del Negro, Marco & Schorfheide, Frank, 2007.
"Forming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities),"
CEPR Discussion Papers
6119, C.E.P.R. Discussion Papers.
- 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.
- Marco Del Negro & Frank Schorfheide, 2008. "Forming Priors for DSGE Models (and How it Affects the Assessment of Nominal Rigidities)," NBER Working Papers 13741, National Bureau of Economic Research, Inc.
- Frank Schorfheide & Marco Del Negro, 2007. "Forming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities)," 2007 Meeting Papers 283, Society for Economic Dynamics.
- Marco Del Negro & Frank Schorfheide, 2006. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," FRB Atlanta Working Paper No. 2006-16, Federal Reserve Bank of Atlanta.
- Marco Del Negro & Frank Schorfheide, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Staff Reports 320, Federal Reserve Bank of New York.
- 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.
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