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Identifying the sources of model misspecification

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

Abstract

Conventional macroeconomic models fail to predict to the Great Recession. Is it because they are misspecified? 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 misspecification via forecast error variance decomposition and marginal likelihood analyses. The 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 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

  • Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
  • Handle: RePEc:eee:moneco:v:110:y:2020:i:c:p:1-18
    DOI: 10.1016/j.jmoneco.2019.01.003
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    Cited by:

    1. Fabio Canova & Filippo Ferroni & Christian Matthes, 2015. "Approximating Time Varying Structural Models With Time Invariant Structures," Working Paper 15-10, Federal Reserve Bank of Richmond.
    2. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," LSE Research Online Documents on Economics 86320, London School of Economics and Political Science, LSE Library.
    3. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    4. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    5. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.
    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. 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.
    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. 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.

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    More about this item

    Keywords

    Misspecification; DSGE models; Forecast error variance decomposition; Marginal likelihood;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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