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

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  • Nikolay Iskrev

Abstract

This paper presents a new approach to parameter identification analysis in DSGE models wherein the strength of identification is treated as property of the underlying model and studied prior to estimation. The strength of identification reflects the empirical importance of the economic features represented by the parameters. Identification problems arise when some parameters are either nearly irrelevant or nearly redundant with respect to the aspects of reality the model is designed to explain. The strength of identification therefore is not only crucial for the estimation of models, but also has important implications for model development. The proposed measure of identification strength is based on the Fisher information matrix of DSGE models and depends on three factors: the parameter values, the set of observed variables and the sample size. By applying the proposed methodology, researchers can determine the effect of each factor on the strength of identification of individual parameters, and study how it is related to structural and statistical characteristics of the economic model. The methodology is illustrated using the medium-scale DSGE model estimated in Smets and Wouters (2007).

Suggested Citation

  • 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.
  • Handle: RePEc:ptu:wpaper:w201032
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    References listed on IDEAS

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    1. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    2. 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.
    3. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2009. "Frequentist inference in weakly identified DSGE models," Working Papers 09-13, Federal Reserve Bank of Philadelphia.
    4. A. Shapiro & M. Browne, 1983. "On the investigation of local identifiability: A counterexample," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 303-304, June.
    5. Bekker, Paul A. & Pollock, D. S. G., 1986. "Identification of linear stochastic models with covariance restrictions," Journal of Econometrics, Elsevier, vol. 31(2), pages 179-208, March.
    6. Anderson, Gary & Moore, George, 1985. "A linear algebraic procedure for solving linear perfect foresight models," Economics Letters, Elsevier, vol. 17(3), pages 247-252.
    7. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    8. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Evaluating the strength of identification in DSGE models. An a priori approach
      by Christian Zimmermann in NEP-DGE blog on 2011-01-23 09:07:09

    Citations

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    Cited by:

    1. Gomes, Sandra & Iskrev, Nikolay & Mendicino, Caterina, 2017. "Monetary policy shocks: We got news!," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 108-128.
    2. Francesco Bianchi & Cosmin Ilut, 2017. "Monetary/Fiscal Policy Mix and Agent's Beliefs," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 26, pages 113-139, October.
    3. Sandra Gomes & Caterina Mendicino, 2011. "Housing Market Dynamics: Any News?," Working Papers w201121, Banco de Portugal, Economics and Research Department.
    4. Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2013. "On Identification of Bayesian DSGE Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 300-314, July.
    5. repec:eee:ecmode:v:64:y:2017:i:c:p:249-269 is not listed on IDEAS
    6. Yasuo Hirose & Atsushi Inoue, 2016. "The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 630-651, June.
    7. Cantore, Cristiano & Levine, Paul & Pearlman, Joseph & Yang, Bo, 2015. "CES technology and business cycle fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 133-151.
    8. Mendicino, Caterina, 2012. "On the amplification role of collateral constraints," Economics Letters, Elsevier, vol. 117(2), pages 429-435.
    9. Marianna Riggi & Sergio Santoro, 2015. "On the Slope and the Persistence of the Italian Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 11(2), pages 157-197, March.
    10. Ercolani, Valerio & Valle e Azevedo, João, 2014. "The effects of public spending externalities," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 173-199.
    11. Elton Beqiraj & Massimiliano Tancioni, 2014. "Evaluating Labor Market Targeted Fiscal Policies in High Unemployment EZ Countries," Working Papers 165, University of Rome La Sapienza, Department of Public Economics.
    12. Isaiah Andrews & Anna Mikusheva, 2014. "Weak Identification in Maximum Likelihood: A Question of Information," American Economic Review, American Economic Association, vol. 104(5), pages 195-199, May.
    13. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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