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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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    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. 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. Iskrev, Nikolay, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Paper Series 2161, European Central Bank.
    6. Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Ratto, Marco, 2019. "Identification versus misspecification in New Keynesian monetary policy models," European Economic Review, Elsevier, vol. 113(C), pages 225-246.
    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. 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.
    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. 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.
    11. 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).
    12. 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.
    13. Hirose, Yasuo & Kurozumi, Takushi, 2021. "Identifying News Shocks With Forecast Data," Macroeconomic Dynamics, Cambridge University Press, vol. 25(6), pages 1442-1471, September.
    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. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    16. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    17. Nikolay, Iskrev, 2014. "Choosing the variables to estimate singular DSGE models: Comment," Dynare Working Papers 41, CEPREMAP.
    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. 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.
    20. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    21. Mendicino, Caterina, 2012. "On the amplification role of collateral constraints," Economics Letters, Elsevier, vol. 117(2), pages 429-435.
    22. Elton Beqiraj & Massimiliano Tancioni, 2014. "Evaluating Labor Market Targeted Fiscal Policies inHigh Unemployment EZ Countries," Working Papers in Public Economics 165, Department of Economics and Law, Sapienza University of Roma.
    23. 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.
    24. Normann Rion, 2020. "Fluctuations in a Dual Labor Market," Working Papers halshs-02570540, HAL.
    25. Herranz, Moisés Meroño & Turino, Francesco, 2023. "Tax evasion, fiscal policy and public debt: Evidence from Spain," Economic Systems, Elsevier, vol. 47(3).

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