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Evaluating the strength of identification in DSGE models. An a priori approach


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  • Nikolay Iskrev

    (Bank of Portugal)

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    The strength of identification in structural models is a reflection of the empirical relevance of the model features represented by the parameters. Weak identification arises when some parameters are nearly irrelevant or nearly redundant with respect to the aspects of reality the model is intended to explain. The strength of identification is therefore not only a crucial requirement for the reliable estimation of models, but also has important implications for model development. This paper proposes a new framework for evaluating the strength of identification in linearized dynamic stochastic general equilibrium (DSGE) models prior to their estimation. In a parametric setting, the empirical implications of a model are contained in the likelihood function, which, for DSGE models, is completely characterized by the underlying structural model. I show how to use standard asymptotic theory to evaluate the theoretical properties of likelihood-based estimators at any point in the parameter space associated with the model. Furthermore, in addition to assessing the informativeness of the likelihood as a whole, I show how to determine which particular features of the data, such as moments of a given variable or a set of variables, are most important for the identification of a given parameter. The methodology is illustrated using a medium-scale business cycle model.

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    Bibliographic Info

    Paper provided by Society for Economic Dynamics in its series 2010 Meeting Papers with number 1117.

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    Date of creation: 2010
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    Handle: RePEc:red:sed010:1117

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    Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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    1. Pablo A. Guerron, 2007. "What You Match Does Matter: The Effects of Data on DSGE Estimation," Working Paper Series, North Carolina State University, Department of Economics 012, North Carolina State University, Department of Economics.
    2. Inoue, Atsushi & Rossi, Barbara, 2011. "Testing for weak identification in possibly nonlinear models," Journal of Econometrics, Elsevier, Elsevier, vol. 161(2), pages 246-261, April.
    3. A. Shapiro & M. Browne, 1983. "On the investigation of local identifiability: A counterexample," Psychometrika, Springer, Springer, vol. 48(2), pages 303-304, June.
    4. Guerron-Quintana, Pablo A. & Inoue, Atsushi & Kilian, Lutz, 2009. "Frequentist Inference in Weakly Identified DSGE Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 7447, C.E.P.R. Discussion Papers.
    5. Pollock, D.S.G., 1988. "The Estimation of Linear Stochastic Models with Covariance Restrictions," Econometric Theory, Cambridge University Press, vol. 4(03), pages 403-427, December.
    6. Nikolay Iskrev, 2009. "Local Identification in DSGE Models," Working Papers, Banco de Portugal, Economics and Research Department w200907, Banco de Portugal, Economics and Research Department.
    7. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
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