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How much Structure in Empirical Models?

In: Palgrave Handbook of Econometrics

Author

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  • Fabio Canova

Abstract

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways, but solved only if DSGEs are completely reparameterized or respecified. The potential misspecification of the structural relationships gives Bayesian methods an edge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have generated the data.

Suggested Citation

  • Fabio Canova, 2009. "How much Structure in Empirical Models?," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 2, pages 68-97, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-0-230-24440-5_2
    DOI: 10.1057/9780230244405_2
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    Citations

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    Cited by:

    1. Leon Podkaminer, 2021. "Dynamic Stochastic General Equilibrium: macroeconomics at a dead end," Bank i Kredyt, Narodowy Bank Polski, vol. 52(2), pages 97-122.
    2. Horacio A. Aguirre & Emilio F. Blanco, 2015. "Credit and Macroprudential Policy in an Emerging Economy: a Structural Model Assessment," BIS Working Papers 504, Bank for International Settlements.
    3. Jean-Sébastien Pentecôte, 2010. "Long-run identifying restrictions on VARs within the AS-AD framework," Post-Print halshs-00554867, HAL.
    4. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    5. Niraj Poudyal & Aris Spanos, 2022. "Model Validation and DSGE Modeling," Econometrics, MDPI, vol. 10(2), pages 1-25, April.
    6. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    7. Marco Cozzi, 2014. "Heterogeneity In Macroeconomics And The Minimal Econometric Interpretation For Model Comparison," Working Paper 1333, Economics Department, Queen's University.
    8. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    9. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    10. Duo Qin, 2022. "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers 249, Department of Economics, SOAS University of London, UK.

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