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Identifying the Sources of Model Misspecification

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  • Rossi, Barbara
  • Inoue, Atsushi
  • Kuo, Chun-Hung

Abstract

In this paper we propose empirical methods for detecting and identifying misspecifications in DSGE models. We introduce wedges in a DSGE model and identify potential misspecification via forecast error variance decomposition (FEVD) and marginal likelihood analyses. Our simulation results based on a small-scale DSGE model demonstrate that our method can correctly identify the source of misspecification. Our empirical results show that the medium-scale New Keynesian DSGE model that incorporates features in the recent empirical macro literature is still very much misspecified; our analysis highlights that the asset and labor markets may be the source of the misspecification.

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  • Rossi, Barbara & Inoue, Atsushi & Kuo, Chun-Hung, 2014. "Identifying the Sources of Model Misspecification," CEPR Discussion Papers 10140, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:10140
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    2. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Bank of England working papers 527, Bank of England.
    3. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    4. 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.
    5. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2021. "Empirical evidence on the Euler equation for investment in the US," Papers 2107.08713, arXiv.org, revised Aug 2022.
    6. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    7. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.
    8. Helena Marques & Gabriel Pino & J. D. Tena, 2018. "Voting with your feet: migration flows and happiness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 163-187, June.
    9. 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.
    10. 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).
    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. Hatcher, Michael & Minford, Patrick, 2023. "Chameleon models in economics: A note," Cardiff Economics Working Papers E2023/10, Cardiff University, Cardiff Business School, Economics Section.

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    Keywords

    Dsge models; Empirical macroeconomics; Model 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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