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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)

Abstract

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.

Suggested Citation

  • Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.
  • Handle: RePEc:red:sed010:1117
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    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

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    Cited by:

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    3. Caterina Mendicino & Sandra Gomes, 2011. "Housing Market Dynamics: Any News?," Working Papers w201121, Banco de Portugal, Economics and Research Department.
    4. 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.
    5. 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.
    6. Afrin, Sadia, 2017. "The role of financial shocks in business cycles with a liability side financial friction," Economic Modelling, Elsevier, vol. 64(C), pages 249-269.
    7. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    8. 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.
    9. Mendicino, Caterina, 2012. "On the amplification role of collateral constraints," Economics Letters, Elsevier, vol. 117(2), pages 429-435.
    10. Afrin, Sadia, 2020. "Does oligopolistic banking friction amplify small open economy's business cycles? Evidence from Australia," Economic Modelling, Elsevier, vol. 85(C), pages 119-138.
    11. 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.
    12. Johannes Hermanus Kemp & Hylton Hollander, 2020. "A medium-sized, open-economy, fiscal DSGE model of South Africa," WIDER Working Paper Series wp-2020-92, World Institute for Development Economic Research (UNU-WIDER).
    13. Massimo Minesso Ferrari, 2020. "The Real Effects of Endogenous Defaults on the Interbank Market," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(3), pages 411-439, November.
    14. 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.
    15. Elton Beqiraj & Massimiliano Tancioni, 2014. "Evaluating Labor Market Targeted Fiscal Policies inHigh Unemployment EZ Countries," Working Papers in Public Economics 165, University of Rome La Sapienza, Department of Economics and Law.
    16. 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.
    17. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    18. Di Bartolomeo, Giovanni & Di Pietro, Marco & Beqiraj, Elton, 2020. "Price and wage inflation persistence across countries and monetary regimes," Journal of International Money and Finance, Elsevier, vol. 109(C).
    19. Normann Rion, 2020. "Fluctuations in a Dual Labor Market," Working Papers halshs-02570540, HAL.
    20. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.

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    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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