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Identifying the Sources of Model Misspecification

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  • Atsushi Inoue
  • Chun-Huong Kuo
  • Barbara Rossi

Abstract

In this paper we propose an empirical method for detecting and identifying misspecification in structural economic models. Our approach formalizes the common practice of adding "shocks" in the model, and identifies potential misspecificacion via forecast error variance decomposition and marginal likelihood anlalyses. The simulation results based on a samall-scale DSGE model demonstrate that our method can correctly identify the source of misspecification. Our empirical results show that state-of-the-art medium-scale New Keynesian DSGE models remain misspecified, pointing to asset and labor markets as the sources of the misspecification.

Suggested Citation

  • Atsushi Inoue & Chun-Huong Kuo & Barbara Rossi, 2015. "Identifying the Sources of Model Misspecification," Working Papers 821, Barcelona School of Economics.
  • Handle: RePEc:bge:wpaper:821
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    3. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    4. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2021. "Empirical evidence on the Euler equation for investment in the US," CAMA Working Papers 2021-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Bank of England working papers 527, Bank of England.
    6. Den Haan, Wouter J. & Drechsel, Thomas, 2021. "Agnostic Structural Disturbances (ASDs): Detecting and reducing misspecification in empirical macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 258-277.
    7. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    8. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    9. Hatcher, Michael & Minford, Patrick, 2023. "Chameleon models in economics: A note," Cardiff Economics Working Papers E2023/10, Cardiff University, Cardiff Business School, Economics Section.
    10. Filippo Ferroni & Jonas D. M. Fisher & Leonardo Melosi, 2022. "Usual Shocks in our Usual Models," Working Paper Series WP 2022-39, Federal Reserve Bank of Chicago.
    11. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.
    12. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.
    13. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.

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    More about this item

    Keywords

    DSGE models; marginal likelihood; misspecification;
    All these keywords.

    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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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