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Variance Decomposition Analysis for Nonlinear Economic Models

Author

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  • Maksim Isakin
  • Phuong V. Ngo

Abstract

In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.

Suggested Citation

  • Maksim Isakin & Phuong V. Ngo, 2020. "Variance Decomposition Analysis for Nonlinear Economic Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1362-1374, December.
    • Handle: RePEc:bla:obuest:v:82:y:2020:i:6:p:1362-1374
    DOI: 10.1111/obes.12369
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    References listed on IDEAS

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    Cited by:

    1. Quinlan Lee, 2025. "Nonlinear Forecast Error Variance Decompositions with Hermite Polynomials," Papers 2503.11416, arXiv.org, revised Oct 2025.
    2. Joshua Bernstein & Michael D. Plante & Alexander W. Richter & Nathaniel A. Throckmorton, 2021. "Countercyclical Fluctuations in Uncertainty are Endogenous," Working Papers 2109, Federal Reserve Bank of Dallas.
    3. Christian Gourieroux & Quinlan Lee, 2025. "Identification of Impulse Response Functions for Nonlinear Dynamic Models," Papers 2506.13531, arXiv.org, revised Jul 2025.
    4. W. A. Razzak, 2021. "Measuring the effect of negative interest rate on New Zealand banks," SN Business & Economics, Springer, vol. 1(3), pages 1-23, March.
    5. Joshua Bernstein & Alexander W. Richter & Nathaniel A. Throckmorton, 2020. "The Business Cycle Mechanics of Search and Matching Models," Working Papers 2026, Federal Reserve Bank of Dallas.

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